Seizure Prediction

Daily Report: August 20,2024
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1. Current Task: Training and testing of the various DL model and tabulate the results.
2. Testing of Patient's data and tabulate the results.
3. Epoch generation of patients.
4. Exploring various DL models used for seizure prediction.
5. Review of seizure prediction.

Daily Report: August 21,2024
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1. Prepared testing data for live seizure prediction.
2. DL Model created for training.
3. Testing single datafiles for seizure prediction using CNN-LSTM model.
4. Train and test data creation for each subject from patient no: 22 to 27.
5. Training and testing of all submerged data.

Daily Report: August 22,2024
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1. Tested the 20% testing data which is submerged for testing and training.
2. Tested the 80% training data from submerged data.
3. Meeting with mani sir about the decision making post processing of seizure prediction.
4. Prepared a writeup about Portable Seizure Prediction System.
5. Explore cognitive training and retraining images.
6. Created a diagram - Cognitive training.

Daily Report: August 22,2024
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1. Created PPT for Neurology Disorder Screening System with Indigenous EEG device development - Present State in India and Outside the India.
2. Prepared a writeup for Cerebral Palsy Registry and AI based treatment and outcome prediction System.
3. A image is generated to illustrate Cerebral Palsy Registry and AI based treatment and outcome prediction System.
4. Explore about how to improve the accuracy by adding features to the seizure prediction system.
5. Testing of seizure prediction system is carried out with 6 labels.
6. Explore through various datasets for seizure prediction system.

Daily Report: August 23,2024
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1. Created PPT for Neurology Disorder Screening System with Indigenous EEG device development - Present State in India and Outside the India.
2. Prepared a writeup for Cerebral Palsy Registry and AI based treatment and outcome prediction System.
3. A image is generated to illustrate Cerebral Palsy Registry and AI based treatment and outcome prediction System.
4. Explore about how to improve the accuracy by adding features to the seizure prediction system.
5. Testing of seizure prediction system is carried out with 6 labels.
6. Explore through various datasets for seizure prediction system.

Daily Report: August 24,2024
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1. Explore through EEG Corpus dataset and BIDS format.
2. Explore through various preprocessing filters.
3. Training and Testing using 7 labels/events with various preprocessing filters.
4. Generated a request for accessing the dataset of TUH EEG Corpus.

Daily Report: August 28,2024
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1. Explore through t-SNE for visualizing high-dimensional data.
2. Developed a code to generate synthetic data with 6 features, 1500 samples, and 3 classes.
3. Training and testing of seizure prediction using various preprocessing filters.
4. Developing of features for the training of models.

Daily Report: August 29,2024
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1. Explore through YOLO algorithm.
2. Explore through best preprocessing techniques which can be used in seizure prediction.
3. Developing the feature "Maximal Lyapunov Exponent".
4. Development of python code to implement the reading of .data file into readable format for epoch cutting.
5. Installation and Configuration of Fortinet SSL-VPN Client on Windows.
6. Exploring how to measure spectral entropy using Shannon's Entropy.
7. Training of CNN-LSTM model.

Daily Report: August 30,2024
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1. Developed the time domain feature "Maximal Lyapunov Exponent".
2. Developing the code for reading a struct file with .data extension.
3. Developing frequency domain features for seizure prediction.
4. Explore through YOLO based journals.
5. Training and Testing of deep learning models.

Daily Report: September 02,2024
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1. Developed the time domain feature and frequency domain features.
2. Training and testing of individual event is carried out and tabulate the results.
3. Exploring the TUH EEG corpus dataset.
4. Explore through the eBrainVoice_V2pt0 codes.

Daily Report: September 03,2024
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1. Training and testing is carried out with 80% and 20% data and tabulated the results.
2. Exploring the TUH EEG corpus dataset.
3. Explore through the bandpass, bandstop, lowpass and highpass filters for preprocessing.

Daily Report: September 04,2024
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1. Training and testing is carried out with 80% and 20% data and tabulated the results.
2. Exploring and downloading the TUH EEG corpus dataset.
3. Explore through the installation of MobaXterm.
3. Run and familiarization of EEGDataViewer for epoch cutting.
4. Exploring and cutting of various dataset includes SCT data for epoch generation.

Daily Report: September 05,2024
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1. Exploring and downloading the TUH EEG corpus dataset.
2. Explore through various MobaXterm commands, working and downloading of data in MobaXterm.
3. Epoch generation of SCTData_28_2_12.
4. Exploring various AAREADME.txt and documents in TUH.

Daily Report: September 06,2024
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1. Exploring through logging and levels of logging.
2. Developed code to configure logger into a file.
3. Configure to log a stack trace.
4. Developed code to create custom logs.
5. Downloading each files of TUH from MobaXterm.

Daily Report: September 09,2024
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1. Downloaded the tuh_eeg_seizure files for training from MobaXterm.
2. Exploring through logging for generating info.
3. Explore to generate license keys for a python app.
4. Explore information about setting up license for application.
5. Gathered information about MIT, Apache 2.0 and GPL.

Daily Report: September 10,2024
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1. Code were developed by using logging.
2. Downloaded the tuh_eeg files v2.0.1 from MobaXterm.
3. Exploring format handler and file handler in logging to change the format of log file.
4. Developing a SerialNumberFormatter to display serial numbers in logging file.
5. Exploring to change the format of logged messages.

Daily Report: September 11,2024
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1. Exploring various anesthesia dataset and downloaded figshare anesthesia data contain 24 files with .mat extension.
2. Documenting details about anesthesia and related datasets.
3. Developing a custom_log_formatter.py to display serial numbers in logging file.
4. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 71 to 150.
5. Copying tuh_eeg_seizure files to My Cloud EX4.
6. Preparing README files for the seizure data which contains all information.

Daily Report: September 12,2024
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1. Successfully developed a logging file to display data used details for training.
2. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 81 to 93.
3. Copying All_backup files to My Cloud EX4 to free the space.
4. Developing a license module to implement authentication and validation.
5. Explore through secure hashing algorithm to implement licensing.

Daily Report: September 13,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 93 to 98.
2. Verifying the license module and checking the errors.

Daily Report: September 13,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 98 to 110.
2. Exploring tSNE for visualization of dataset.
3. Developed a code to implement tSNE. Got errors "ValueError: perplexity must be less than n_samples".
4. Training and testing using EEGNET model.
5. Explore through various preprocessing filters.
6. Developed a sample code that implementing the iris dataset from sklearn.

Daily Report: September 19,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 110 to 117.
2. Exploring tSNE for visualization of dataset.
3. Implementing tSNE, visualizing different types of seizures on a plot.
4. Training and testing using EEGNET model.
5. Discussion about progress bar, Yolo algorithm and seizure prediction.
6. Installation of Pycharm version 2024.2.1 for development purpose.

Daily Report: September 20,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 117 to 122.
2. Training and testing using EEGNET model using 80% , 20% dataset.
3. Developed code to implement t-SNE visualization of dataset.
4. Familiarization of ebrainVoice version3 code.
5. Exploring the code for the creation of progress bar.

Daily Report: September 23,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 122 to 123.
2. Training and testing using EEGNET model using 80% , 20% dataset.
3. Copying TUH EEG data to EX4.
4. Exploring different functions of ebrainVoice version3 code.
5. Exploring the code for the creation of progress bar.
6. Exploring t-SNE algorithm for model creation.

Daily Report: September 24,2024
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1. Attended the ISO-awareness training program.
2. Training and testing using EEGNET with decision window size=12secs.
3. Copying the TUH data to the EX4.
4. Exploring the development of the progress bar.

Daily Report: September 25,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 123 to 125.
2. Training and testing using EEGNET model using 80% , 20% dataset with decision window size=12secs.
3. Copying TUH EEG data to EX4.
4. Exploring different functions of ebrainVoice version3 code.
5. Exploring the code for the creation of progress bar.
6. Developing the progress bar and solving the error "self.chart_frame = tk.Frame(master) NameError: name 'master' is not defined".

Daily Report: September 26,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 125 to 131.
2. Training and testing using EEGNET model using 80% , 20% dataset with decision window size=12secs.
3. Copying TUH EEG data to EX4.
4. Exploring the code for the creation of progress bar.
5. Developing the progress bar.
6. Training and testing ebrainv3 for the development of progress bar.

Daily Report: September 27,2024
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1. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 131 to 135.
2. Training and testing using EEGNET model using 80% , 20% dataset with decision window size=12secs.
3. Training and testing with separate IED and EZ folders.
4. Copying TUH EEG data to EX4.
5. Exploring how t-SNE works and handle features.
6. Developing code to implement t-SNE , feature extraction from t-SNE included.

Daily Report: September 30,2024
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1. Training the model with data of all subjects which has high accuracy when tested individually.
2. When performing training got the error "pandas.errors.InvalidIndexError: Reindexing only valid with uniquely valued
Index objects"
3. Downloading TUH_EEG v2.0.1 data from Mobaxterm from 135 to 137.
4. Training and testing with separate IED and EZ folders.
5. Copying TUH EEG data to EX4.
6. Exploring how t-SNE works and handle features when the data is given in the form of signals.
7. Training and testing is performed for individual class and tabulated the results.

1. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 137 to 140 to the local environment.
2. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
3. Generated image Epochs using CVAT and import the EEG data for labelling.
4. Implementing the code to run YOLO algorithm.
5. Installing the ultralytics package, which is essential for working with the YOLOv8n model.
6. Resolving pip installation issues in PyCharm for ultralytics.

Daily Report: October 01,2024
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1. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 137 to 140 to the local environment.
2. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
3. Generated image Epochs using CVAT and import the EEG data for labelling.
4. Implementing the code to run YOLO algorithm.
5. Installing the ultralytics package, which is essential for working with the YOLOv8n model.
6. Resolving pip installation issues in PyCharm for ultralytics.

Daily Report: October 03,2024
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1. Generated image Epochs using CVAT and import the EEG data for labelling.
2. Resolving pip installation issues in PyCharm for ultralytics.
3. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 140 to 142 to the local environment.
4. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
5. Exploring YOLO algorithm for image processing.
6. Exploring methods for EEG signals to video conversion.

Daily Report: October 04,2024
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1. Integrating t-SNE with MiMe application.
2. Exploring through the code of image generation UI for image epoch generation.
3. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 142 to 145 to the local environment.
4. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
5. Exploring YOLO algorithm for image processing.

Daily Report: October 09,2024
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1. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 145 to 146 to the local environment.
2. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
3. Exploring the progress bar code for error corruption.
4. Integrating the progress bar code with eBrain version3 code.
5. Code integration is successfully completed, the barchart is displayed but the UI became "Python isn't responding" state.

Daily Report: October 10,2024
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1. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 146 to 147 to the local environment.
2. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
3. The integration has been completed successfully, and the bar chart is now displayed; however, the UI becomes unresponsive, indicating a "Python is not responding" state.
4. Investigating the progress bar code for potential errors or corruption.
5. Exploring the code for labels and frames used in the UI.
6. Implementing the code to display a progress bar adjacent to the label "AI PLATFORM.
7. During the testing, the following error occurred: KeyError: 'feature' when attempting to access save_option_json['saveOptions']['feature']["is_feature"].

Daily Report: October 14,2024
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1. Implementing the code to display a progress bar on the bottom of the UI.
2. Solving the "UI not responding" problem using after to update the UI once the computations are done.
3. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 147 to the local environment.
4. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.

Daily Report: October 15,2024
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1. Implementing a seizure_progress_bar_Callback function to incorporate sender and receiver threads for processing predictions.
2. Generating a queue to send the prediction results from the processing thread to the progress bar callback.
3. Downloading TUH_EEG v2.0.1 Data via MobaXterm in the range from 147 to 150 to the local environment.
4. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
5. Exploring the implementation and scope of a thread in a program.

Daily Report: October 16,2024
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1. Exploring the issue with memory allocation or resource management when creating graphical elements in Tkinter or when integrating with Matplotlib.
2. Troubleshooting the issue "Fail to allocate bitmap" by the progress bar dynamically appends to the previous graph on the same canvas.
3. Downloading tuh_eeg_abnormal data via MobaXterm to the local environment.
4. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
5. Exploring the stacked bar chart concept.

Daily Report: October 17,2024
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1. Implementing the progress bar with dynamic bar appending.
2. Downloaded tuh_eeg_artifact, tuh_eeg_epilepsy data via MobaXterm to the local environment.
3. Downloading tuh_eeg_events data via MobaXterm to the local environment.
4. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
5. Troubleshooting the issue where the function call did not execute properly.

Daily Report: October 18,2024
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1. Creating a license module that uses secure hashing algorithms for authentication and validation.
2. Downloaded the entire TUH EEG dataset via MobaXterm to the local environment.
3. Transferring the downloaded TUH EEG dataset to the EX4 storage unit.
4. Troubleshooting the issue to ensure the call self.seizure_progress_bar_Callback() after put data into the queue, and make sure that the result_individual_mode_merged dataFrame send to the
queue contains a 'predictions' column that holds the correct values.

Daily Report: October 21,2024
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1. Troubleshooting the issue in the development of progress bar :
File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\mttkinter\mtTkinter.py", line 136, in call
self._tk._event_queue.put((self._attr, args, kwargs, response_queue), True, 1)
File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\queue.py", line 148, in put
raise Full
queue.Full
2. "queue.Full" error occurs when the mttkinter event queue reaches its capacity, due to frequent or rapid updates to the Tkinter GUI from a background thread.
3. Exception in thread Thread-13 (run_seizure_progress_bar).

Daily Report: October 22,2024
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1. Current Task: Implementing a progress bar that visualizes results from the final prediction.
2. Addressing queue overflow issues occurring when threads enqueue prediction results into the result queue - "in put
raise Full queue.Full".
3. Codebase analysis and optimization for real-time progress bar functionality.
4. Designing and refining the user interface to support and display live progress bar updates efficiently.

Daily Report: October 23,2024
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1. Exploring the README file of the TUH Seizure dataset to use it for training the seizure prediction model.
2. Analyzing the concept of using different data sizes for seizure prediction without segmenting epochs.
3. Designing and exploring the concept of live predictions using a progress bar.
4. Troubleshooting the issue where the queue becomes full when adding prediction values.
5. Exploring the excel sheets of TUH_Data to identify the start and end times of seizures and verifying the labels for accurate data identification.

Daily Report: October 24,2024
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1. Exploring and familiarizing with the code for LiveUI and SimulatorUI components.
2. Troubleshooting the "MCI initialization error" encountered in the Playsound module within LiveUI.
3. Conducting seizure predictions in LiveUI using epochs extracted from SCT data.
4. Analyzing the number of channels in the SCT data to be used as input for LiveUI.
5. Created a PPT of Bio-Medical Signal Analyzer for Seizure Prediction.

Daily Report: October 25,2024
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1. Conducted a exploration of the complete codebase for LiveUI.
2. Incorporated a group box and radio buttons to facilitate the selection of seizure detection and prediction modes.
3. Implementing the frame for integrating a progress bar into the execution page of the application.
4. Modified the PPT of Bio-Medical Signal Analyzer for Seizure Prediction.
5. Solved the issues related to the audio when performing seizure detection in LiveUI.

Daily Report: October 29,2024
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1. Conducted a detailed exploration through the code of screen5.py of liveui and identified the queue which stores the events.
2. Troubleshooting the issue "Exception in thread Thread-1 (update_event_label):
Traceback (most recent call last):
File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 1038, in _bootstrap_inner
self.run()
File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 975, in run
self._target(*self._args, **self._kwargs)
File "E:\svn repository\live_ui\view\screen5.py", line 358, in update_event_label
if gl.audio_alert_checked and gl.text_alert_checked and gl.progress_bar_alert_checked:
^^{^^}^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'globals' has no attribute 'progress_bar_alert_checked'. Did you mean: 'progressbar_alert_checked'?"

Daily Report: October 30,2024
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1. Current Task : Implementing the progress bar in the LiveUI.
2. Troubleshooting the issue "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 975, in run
self._target(*self._args, **self._kwargs)
TypeError: screen5.update_label() missing 1 required positional argument: 'text'"
3. Tracing the issue in "Traceback (most recent call last):
File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 1038, in _bootstrap_inner
self.run()
File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\threading.py", line 975, in run
self._target(*self._args, **self._kwargs)
File "E:\svn repository\live_ui\execute\machinePipeline.py", line 150, in general_producer_thread
raise (RuntimeError("Cant find EEG stream"))
RuntimeError: Cant find EEG stream"

Daily Report: October 31,2024
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1. The implementation of the progress bar is ongoing.
2. The signal self.update_label_signal.emit("iktal(Seizure)") indicates an issue.
3. The progress bar is visible during execution, but the UI becomes unresponsive.
4. Making modifications to the UI to enhance its responsiveness.
5. Training the model to achieve a high percentage of accuracy for integration into the Live UI.
6. Exploring global declarations and the use of self in the program.

Daily Report: November 01,2024
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1. Troubleshooting UI Unresponsiveness: Checking if the UI becomes unresponsive during specific actions or after certain events.
2. Tracing Issues in connect and emit, ensuring they handle events correctly and check for any potential issues in the callback functions.
3. Testing the event labels to ensure they are displayed correctly and that the UI remains responsive.

Daily Report: November 04,2024
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1. Implemented the progress bar in the LiveUI.
2. Troubleshooting the issue "event_label Inter iktal
Process finished with exit code -1073741819 (0xC0000005)".
3. Implementing the QThread signals to update the progress bar in the main thread safely.
4. Tracing the issue "File "E:\svn repository\live_ui\view\screen5.py", line 479, in update_event_label
self.append_to_stacked_bar(event_label, 20) # Adjust 20 to desired increment
^^{^^}^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: screen5.append_to_stacked_bar() takes from 1 to 2 positional arguments but 3 were given"

Daily Report: November 05,2024
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1. Successfully completed the implementing of a dynamic progress bar in the LiveUI.
2. Dynamically appending segments to the progress bar, with each segment using the corresponding color to represent progress within a stacked layout.
3. Troubleshooted and resolved memory access violations related to the progress bar's stylesheet.
4. Traced and resolved UI unresponsiveness issues.

Daily Report: November 06,2024
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1. Discussion with Subhasree ma'am about the project "Comprehensive Brain-Computer Interface (BCI) - Motor and Somatosensory Based Robotic Upper Limb".
2. Preparaing a writeup about "Comprehensive Brain-Computer Interface (BCI) - Motor and Somatosensory Based Robotic Upper Limb".
3. Exploring the TUH EEG dataset involves a thorough analysis of the AREADME.txt file to extract key information and insights about the dataset’s structure and characteristics.
4. Preparing a Word document to document key information about the TUH EEG dataset, including file structure, sampling frequency, and dataset organization details.
5. Analyzing montage configurations, specifically the bipolar montage, within the TUH EEG dataset.

Daily Report: November 07,2024
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1. Writing a project proposal on a comprehensive Brain-Computer Interface (BCI) system for motor and somatosensory-based control of a robotic upper limb.
2. Expanding content related to the development of an indigenous robotic arm.
3. Investigating the concept of error potential in brain-computer interface applications.
4. Exploring a neuro-robotics-based BCI system for controlling an assistive wheelchair.
5. Familiarizing with the epoch-cutting tool.

Daily Report: November 08,2024
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1. Drafting a project proposal on the topic, "Neuro-Robotics-Based BCI Assistive Wheelchair Control System."
2. Designing and editing a visual representation to explain the methodology of the Neuro-Robotics-Based BCI Assistive Wheelchair Control System in detail.
3. Calculating and tabulating the number of channels for each subject across files in Excel sheets.
4. Revising the project proposal titled "Comprehensive Brain-Computer Interface (BCI) System for Motor and Somatosensory-Based Control of a Robotic Upper Limb" based on provided feedback.
5. Exploring the Statistical report of Stroke and Orthopedic Disabilities as of 2024.

Daily Report: November 11,2024
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1. Documenting the specifics of the TUH EEG, Seizure, and Abnormal EEG datasets.
2. Analyzing the tuh_seizure dataset and its associated documentation to determine its suitability for training.
3. Testing the epoch cutting tool for generating epochs from TUH EDF files.
4. Attended a session demonstrating the use of SVN.

Daily Report: November 12,2024
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1. Discussion with Vaishnavi regarding the development of an .exe for license management.
2. Model training conducted using 80% of the dataset with the SVM algorithm.
3. Implementation of licensing functionality, including key generation using the hashlib library.
4. Developing the user interface for license validation using Tkinter.

Daily Report: November 13,2024
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1. Model training and testing are conducted on an 80/20 split of the dataset using the Random Forest and CatBoost algorithms.
2. A line diagram is being generated to illustrate the proposal titled "Comprehensive Brain-Computer Interface (BCI) System for Motor and Somatosensory-Based Control of a Robotic Upper Limb."
3. The implementation of licensing using the SHA-256 hashing algorithm is currently in progress.

Daily Report: November 18,2024
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1. Developing a Gantt Chart: Refining the Gantt chart for the project proposal titled "Comprehensive Brain-Computer Interface (BCI) System for Motor and Somatosensory-Based Control of a Robotic Upper Limb".
2. Preparing the Budget Justification Document: Drafting a detailed "Justification of Budget" document for submission to CDAC Thiruvananthapuram, incorporating the financial requirements and allocations relevant to project collaboration with CDAC and NIMHANS.
3. Completing the Declaration Forms: Filling out and finalizing the declaration forms required for project submission and compliance with CDAC and NIMHANS guidelines.

Daily Report: November 19,2024
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1. Preparing the Budget Justification document, outlining the financial requirements, justifications, and allocations associated with the collaborative project between CDAC and NIMHANS.
2. Performing the calculation, verification, and detailed documentation of the budget for CDAC and NIMHANS in an Excel format, ensuring accuracy and alignment with project requirements.
3. Developed a line diagram illustrating the methodology of "Brain-Computer Interface (BCI) and Haptics-Enabled Robotic Arm for Stroke Rehabilitation".

Daily Report: November 20,2024
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1. Integrating datasets from subjects 24 and 26 to construct a 80% training set and 20% testing set for seizure prediction.
2. Analyzing channel counts for subjects 23 to 27 and systematically documenting the findings in an Excel sheet.
3. Conducting training and validation using the SVM and random forest algorithm on the combined dataset of subjects 24 and 26.
4. Diagnosing and resolving issues encountered during SVM model training " data_file = gl.loaded_signal[start_row_index:end_row_index, i] IndexError: index 16 is out of bounds for
axis 1 with size 16".
5. The dataset from subject 27 is processed for upsampling to a sampling frequency of 1000 Hz.

Daily Report: November 21,2024
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1. The training and testing procedures were conducted using a custom channel configuration for subject 27, which consists of 68 channels.
2. The issue was debugged during training with upsampled data, where an error occurred in the file "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-
packages\sklearn\utils\validation.py" at line 778 in the check_array function. The error traceback showed:
dtype_orig = np.result_type(*dtypes_orig) ^^{^^}^^^^^^^^^^^^^^^^^^^^^^^
File "<__array_function__ internals>", line 180, in result_type
ValueError: at least one array or dtype is required
3. The entire upsampled dataset was consolidated into a single directory for the purpose of training.
4. Training and testing were performed using the Random Forest algorithm, and documentation was maintained to record the accuracy results.
5. Training was conducted using the CatBoost algorithm to assess whether there was an improvement in accuracy.

Daily Report: November 22,2024
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1. Analyzing the epochs of subject 27 for seizure identifications in EEGDataViewer.
2. Diagonsed and troubleshotting an error in the EEgDataViewer "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\matplotlib\axes\_base.py", line 504, in _plot_args
raise ValueError(f"x and y must have same first dimension, but "
ValueError: x and y must have same first dimension, but have shapes (1600, 67) and (67,)"
3. Identifying the issue of 28Mib in the pycharm by adjusting the memory settings.
4. Implementing the licensing and solving the issue that the generation of license key everytime when clicking the submit button.

Daily Report: November 23,2024
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1. Resolved the issue where a new license key was generated each time the submit button was clicked.
2. Modified the code to save the license key to a JSON file rather than requiring the user to enter the key each time.
3. Troubleshot the issue preventing the license UI from opening after the key was entered once, and implemented a fix to ensure proper UI behavior.

Daily Report: November 25,2024
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1. Designing and implementing the user interface (UI) for license input and validation functionality.
2. Conducting rigorous validation of the UI and entered license key for accuracy.
3. Implementing robust mechanisms to detect and prevent date tampering during license validation.
4. Testing the logical flow and functionality triggered by the 'Submit' button click event.
5. Exploring advanced key generation algorithms and tools for seamless integration into the licensing module.

Daily Report: November 26,2024
------------------------------------------------------------------------------------------------------------------------
1. Developed a Tkinter-based graphical user interface with input fields and a display widget for auto-generated license keys.
2. Implemented an algorithm to generate a randomized 16-character alphanumeric license key.
3. Integrated SHA-256 encryption to securely hash license details and stored the output in an encrypted file.
4. Explored the theoretical framework of inner speech.
5. Analyzed the functionality and practical implementation of the Support Vector Machine (SVM) algorithm.

Daily Report: November 27,2024
------------------------------------------------------------------------------------------------------------------------
1. Design and implement an overlay frame with a background image and functional UI elements for input and actions in the license key generation tool.
2. Develop a secure encryption mechanism using the Fernet encryption algorithm for protecting the license key file.
3. Identify and resolve the issue preventing the creation of the encryption key file, ensuring seamless file generation.
4. Implement field validation in the UI, including MAC address format verification to conform to the standard pattern (AA-BB-CC-DD-EE-FF), and configure a read-only field for displaying the
generated license key.
5. Analyze and refine the use of the Fernet encryption algorithm to optimize the process of encrypting the license key file.

Daily Report: November 28,2024
------------------------------------------------------------------------------------------------------------------------
1. Acquiring expertise in the implementation and evaluation of inner speech training and testing methodologies as part of a work transfer from Dhanya.
2. Familiarization with the data structure of the InnerSpeech dataset.
3. Understanding the processes for training and testing using the InnerSpeech dataset in the MindMe software.
4. Exploration of data visualization techniques, including histograms, confusion matrices, and scatterplots.
5. Study of the upsampling methods implemented in MindMe.

Daily Report: November 29,2024
------------------------------------------------------------------------------------------------------------------------
1. Discussion with Dhanya regarding the work transfer and the tasks to be performed post-transfer.
2. Investigating the concepts of continuity checking and excluding specific events within the MindMe software framework.
3. Gaining familiarity with various classifiers utilized in model creation and training using MindMe.
4. Exploring additional options available in MindMe, such as balancing preparation and relaxation states, avoiding imbalanced datasets, managing "eyes open" versus "eyes closed" conditions, and working with merged file datasets.

Daily Report: December 02,2024
------------------------------------------------------------------------------------------------------------------------
1. Review and analysis of documented data with a focus on the accuracy metrics and their contextual implications.
2. Resampling 400-epoch data to a 4096 Hz sampling rate.
3. Investigation of encryption algorithms as alternatives to the Fernet algorithm for licensing, assessing suitability and security strength.
4. Exploration of RSA algorithm implementation for secure key exchange and licensing enhancements.

Daily Report: December 03,2024
------------------------------------------------------------------------------------------------------------------------
1. The training process is conducted using a combined dataset of volunteer and patient inner speech data, with 400 epochs.
2. Preparing a new dataset to test the Random Forest-trained model.
3. Upsampling is performed on new patient data, specifically from samples 126 to 139, to evaluate the trained model.
4. Documenting the accuracy metrics obtained during training and testing phases.
5. Exploring the RSA algorithm to understand its functionality and assess the efficiency of the encryption process.

Daily Report: December 04,2024
------------------------------------------------------------------------------------------------------------------------
1. The training and testing processes were conducted using the Random Forest algorithm on the complete dataset as well as on Event 2 data.
2. The accuracies obtained during training and testing were systematically documented.
3. MATLAB 2018 was installed to review and analyze the code for optimizing training efficiency. A cracked version was successfully installed for this purpose.
4. The BMI subvocal recognition code is being explored in MATLAB 2018 and identified the function used for browsing the model folder.
5. Reviewing the SeizurePredictor_V1.0 code to understand its implementation in MATLAB is ongoing.

Daily Report: December 05,2024
------------------------------------------------------------------------------------------------------------------------
1. Attended a meeting about PRSG, including familiarization with procedures and workflows.
2. Training models using Random Forest and BiGRU algorithms.
3. Structuring an Excel document to store the accuracies obtained from training and testing.
4. Implementing the training in multiple PC's for various models.

Daily Report: December 06,2024
------------------------------------------------------------------------------------------------------------------------
1. Training is in progress of BiGRU, random forest, catboost.
2. Tabulating the results in excels.
3. Created a statistical report of data for PRSG.
4. Troubleshooted the errors encountered during the training.
5. Solving the issue find in the inner speech data during upsampling.

Daily Report: December 07,2024
------------------------------------------------------------------------------------------------------------------------
1. Performing training and testing in multiple PC's using various classifiers and dataset.
2. Exploring different techniques to improve the accuracy of the model.
3. Performing deep learning approach training using combined dataset.

Daily Report: December 09,2024
------------------------------------------------------------------------------------------------------------------------
1. Exploring research papers to identify the most suitable features for inner speech data.
2. Training a Random Forest model using various datasets.
3. Investigating the functionality of PCA and ICA.
4. Discussing activity assigning for the IEEE conference.

Daily Report: December 10,2024
------------------------------------------------------------------------------------------------------------------------
1. Random Forest and CatBoost algorithms are being trained under varying configurations and experimental scenarios to optimize performance.
2. Upsampling techniques are applied to newly added test files within the range 126-139 to balance the dataset and enhance model evaluation.
3. The dataset is being reorganized into a structured format to facilitate systematic testing of Random Forest and CatBoost models.
4. The accuracy metrics and experimental details are being meticulously documented in an Excel file for comprehensive analysis and reporting.

Daily Report: December 11,2024
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the issue "File "C:\Users\HP\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py", line 161, in _assert_all_finite
raise ValueError(msg_err)
ValueError: Input X contains NaN.
RandomForestClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values
"
2. Partition the dataset 140 to 161 as new data for testing.
3. Analysis the NaN values in the dataset for troubleshooting the error while testing the model.
4. Training is performed using random forest and catboost and documentation is carried out simultaneously.
5. Generating a word document containing list of works that performed during training.

Daily Report: December 12,2024
------------------------------------------------------------------------------------------------------------------------
1. Training is conducted using a bandpass filter to preprocess data, followed by model training and testing using CatBoost and Random Forest algorithms.
2. Preparing an Excel sheet to document model accuracies systematically.
3. Creating a Word document to compile accuracy results for presentation purposes.
4. Developing an Excel sheet to document individual patient and volunteer IDs along with their respective accuracies.
5. Segmenting the dataset into individual ID-based subsets for patients and volunteers to facilitate analysis.

Daily Report: December 13,2024
------------------------------------------------------------------------------------------------------------------------
1. Conducting experiments on three distinct data subsets: volunteer-only data, patient-only data, and a combined dataset comprising both patient and volunteer data.
2. Utilizing CatBoost and Random Forest algorithms for testing the aforementioned datasets.
3. Validating the trained CatBoost and Random Forest models against data from individual patients and volunteers based on their unique IDs.
4. Preparing technical content for compering in the IEEE conference.
5. Addressing and resolving challenges related to selecting and utilizing datasets not explicitly browsed during the testing phase.

Daily Report: December 14,2024
------------------------------------------------------------------------------------------------------------------------
1. Conducted model evaluation individually for each patient and volunteer ID up to ID 125 using the Random Forest algorithm.
2. The dataset was partitioned into an 80-20 split for training and excluded testing, ensuring mutually exclusive subsets.
3. Verified that the test set is not present in the training dataset to maintain dataset integrity and prevent data leakage.
4. Implemented training and testing workflows using the Random Forest and CatBoost algorithms on the 80-20 split dataset with excluded data for testing.
5. Recorded accuracy metrics systematically in an Excel sheet for comprehensive documentation and analysis.

Daily Report: December 16,2024
------------------------------------------------------------------------------------------------------------------------
1. Conducted model training using the Random Forest algorithm on the patient dataset.
2. Performed testing on the patient dataset using the CatBoost model and encountered a label series error.
3. Troubleshot the label series error and re-trained the CatBoost model for evaluation.
4. Drafted a Word document to document the accuracy metrics and comparative performance of various models.
5. Validated the accuracy and performance of models across multiple datasets.
6. Prepared the script for compering at the IEEE conference.

Daily Report: December 17,2024
------------------------------------------------------------------------------------------------------------------------
1. Testing the model with full frequency using the CatBoost algorithm and patient dataset.
2. Documenting the accuracies and relevant information in a Word document.
3. Preparing the script for presentation at the IEEE conference.
4. Troubleshooting the issue encountered during testing in the classify.py file.
5. Creating a PowerPoint presentation with results for the CatBoost and Random Forest models.
6. encountered an issue while testing the catboost model "File "_catboost.pyx", line 4761, in _catboost._CatBoost._base_predict
File "_catboost.pyx", line 4768, in _catboost._CatBoost._base_predict
_catboost.CatBoostError: C:/Go_Agent/pipelines/BuildMaster/catboost.git/catboost/libs/data/model_dataset_compatibility.cpp:72: Feature 78 is present in model but not in pool."

Daily Report: December 18,2024
------------------------------------------------------------------------------------------------------------------------
1. Participated in the PRSG meetings and documented the suggestions and feedback provided by the members.
2. Completed the final draft of the compering script for the IEEE conference.
3. Developed an Excel spreadsheet detailing the schedule of PPT support for the two-day event.
4. Attended a final meeting for the IEEE conference to discuss the compering and PPT Support.

Daily Report: December 23,2024
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the issue " File "E:\svn repository\eBrainVoice_V3pt0\eBrainVoice\module\src\feature\Feature_Pipeline.py", line 479, in feature_extractor
df_all_feat_single_file_merged = pd.concat([df_all_feat_single_file_merged, label_series], axis=1)
^^{^^}^^^^^^^
NameError: name 'label_series' is not defined"
2. Testing is conducted using the CatBoost algorithm on a dataset split with an 80-20 ratio, without any preprocessing.
3. Evaluating the performance of the trained CatBoost model to verify whether the errors have been resolved.

Daily Report: December 24,2024
------------------------------------------------------------------------------------------------------------------------
1. Conducting performance testing of the CatBoost model under various dataset scenarios to evaluate its effectiveness and robustness.
2. Validating the functionality and security of the license key generator implementation.
3. Investigating advanced encryption algorithms as alternatives to the Fernet encryption method for enhanced security.
4. Compiling a detailed media report summarizing key outcomes and highlights of the IEEE conference.
5. Troubleshooting a error occured when testing is performed using new dataset "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\catboost\core.py", line 1775, in _base_predict
return self._object._base_predict(pool, prediction_type, ntree_start, ntree_end, thread_count, verbose, task_type)
^^{^^}^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "_catboost.pyx", line 4761, in _catboost._CatBoost._base_predict
File "_catboost.pyx", line 4768, in _catboost._CatBoost._base_predict
_catboost.CatBoostError: C:/Go_Agent/pipelines/BuildMaster/catboost.git/catboost/libs/data/model_dataset_compatibility.cpp:72: Feature 78 is present in model but not in pool."

Daily Report: December 26,2024
------------------------------------------------------------------------------------------------------------------------
1. Preparation of professional, engaging posts about the IEEE conference for platforms such as Twitter, Facebook, and LinkedIn, highlighting key takeaways and event highlights.
2. Identification and curation of high-quality images relevant to the conference for use in social media to enhance engagement.
3. Validation of the system using new data, explicitly excluding Subject ID 137's Event 8 from the dataset, to investigate and resolve identified errors.
4. Comprehensive verification of PRSG meeting minutes to ensure accuracy and identify any discrepancies or errors in the documented records.
5. Review and analysis of the assigned post-conference activities, as outlined in the Excel sheet, to track progress.

Daily Report: December 27,2024
------------------------------------------------------------------------------------------------------------------------
1. The model is trained using a patient-specific dataset without applying any preprocessing steps.
2. The Random Forest model is trained and tested on an 80-20 split dataset without incorporating preprocessing.
3. Accuracy metrics are analyzed alongside their respective hyperparameters to evaluate the performance and determine the optimal procedure, as well as to identify the most suitable dataset.
4. Exploring the theoretical concepts and methodologies related to Graph Convolutional Networks (GCNs).
5. Exploring in identifying the journal papers that employ Graph Convolutional Networks (GCNs) with EEG data.

Daily Report: December 30,2024
------------------------------------------------------------------------------------------------------------------------
1. Investigated the concepts of Graph Neural Networks (GNN) and their associated parameters.
2. Performed training and testing using a dataset treated as new data.
3. Explored various encryption algorithms and their implementation for licensing.
4. Prepared and curated media reports and photographs from the inauguration of the IEEE conference.

Daily Report: December 31,2024
------------------------------------------------------------------------------------------------------------------------
1. Exploring the working principles of Graph Neural Networks (GNNs).
2. Exploring the detailed steps for training a GNN model using EEG data: Graph creation, Feature extraction, Message passing between nodes, Loss function optimization using backpropagation,
Emphasis on inner speech detection and leveraging EEG data characteristics.
3. Understanding the Hyperparameter Tuning for GNN Models: Identification of key parameters to enhance model accuracy and mitigate overfitting.
4. Summarizes your focused exploration of GNNs and their application to EEG data, providing a roadmap for further development and experimentation in the project.
5. Prepared a report for the 'News of the Day,' which includes detailed coverage of the IEE Conference, accompanied by relevant images.
6. Training and testing is performed using random forest model with patient only dataset and various scenarios are carried out.

Daily Report: January 01,2025
------------------------------------------------------------------------------------------------------------------------
1. Review and discussion about the graph Neural Network.
2. Understanding where to implement the licensing module in the version4 software.

Daily Report: January 02,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the errors while changing the licensing code from version3 to version4
1."C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\numba\__init__.py", line 42, in _ensure_critical_deps
raise ImportError("Numba needs NumPy 1.24 or less")
ImportError: Numba needs NumPy 1.24 or less

2.File "E:\svn repository\eBrainVoice_V3pt0\eBrainVoice\module\src\feature\FrequencyDomainFeatures.py", line 23, in <module>
from scipy.integrate import simps
ImportError: cannot import name 'simps' from 'scipy.integrate' (C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\scipy\integrate\__init__.py)

3.Traceback (most recent call last):
File "E:\svn repository\eBrainVoice_V4pt0\eBrainVoice\module\src\view\KeyGenerator.py", line 106, in <module>
bg_image = bg_image.resize((800, 600), Image.ANTIALIAS)
^^{^^}^^^^^^^^^^
AttributeError: module 'PIL.Image' has no attribute 'ANTIALIAS' in the key generator tool.

4.File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\tkinter\__init__.py", line 1948, in call
return self.func(*args)
^^{^^}^^^^^^^^^^^
File "E:\svn repository\eBrainVoice_V4pt0\eBrainVoice\module\src\view\License.py", line 142, in update_bg_image
bg_image = bg_image.resize((popup.winfo_width(), popup.winfo_height()), Image.ANTIALIAS)
^^{^^}^^^^^^^^^^
AttributeError: module 'PIL.Image' has no attribute 'ANTIALIAS' in the license activation module.

2.Reviewing and revising the concepts, implementation and integration of license module and key generation tool.

Daily Report: January 03,2025
------------------------------------------------------------------------------------------------------------------------
1. Development involves combining the licensing interface with the login interface to create a licensed version.
2. Implementing logic to enforce a one-time activation process and limit the number of activations to a single instance per license.
3. Investigating various secure methods to persist and verify license activation data.
4. Researching and implementing strategies to store and retrieve activation status using the Windows system registry as a secure and accessible storage mechanism.
5. Coding the licensing functionality to leverage the Windows system registry for storing and validating activation status during application execution.

Daily Report: January 06,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the encountered problem when installing the Python package "Psychopy" in the Python Package tool window.
SSVEP_Stimuli\.venv\Scripts\activate : The module 'SSVEP_Stimuli' could not be loaded. For more information, run
'Import-Module SSVEP_Stimuli'.
At line:1 char:1
+ SSVEP_Stimuli\.venv\Scripts\activate
+ ~_~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ CategoryInfo : ObjectNotFound: (SSVEP_Stimuli\.venv\Scripts\activate:String) [], CommandNotFoundExcepti
on
+ FullyQualifiedErrorId : CouldNotAutoLoadModule
2. Troubleshooting the error where the Python packaging tool 'setuptools' is not found. The error message indicates that psychopy 2024.2.4 has a requirement for setuptools==70.3.0, but the system has setuptools 41.2.0, which is incompatible.
3. Implementing the licensing functionality on the login page, using the license key generated by the key generation tool.
4. The key generation tool has been modified to update the license expiration date format to dd-mm-yy.
5. Encountered an error in the licensing process, where the message "FAILED TO ACTIVATE LICENSE: strptime() argument 1 must be str, not None" suggests that the strptime() function is receiving a None value instead of a valid string input.

Daily Report: January 07,2025
------------------------------------------------------------------------------------------------------------------------
1. The user manual for the project titled "A Brain-Machine Interface Enabled Assistive Communication System for Children with Special Needs" is being reviewed and updated to align with the operational workflow of the MindMe software.
2. The model is being trained and evaluated using a Random Forest classifier across multiple dataset configurations to assess performance under varying scenarios.
3. A date tampering prevention mechanism is being implemented in the licensing module to restrict users from altering system dates to bypass licensing restrictions.
4. The date tampering prevention mechanism, along with associated tampering detection functions, is undergoing validation to ensure robustness and accuracy in identifying and mitigating unauthorized system date modifications.
5. Diagnosing and resolving errors encountered during the execution of the license UI, which is triggered upon license activation.
6. Participated in discussions about web development, focusing on the use of technologies such as Bootstrap, Flask, and related frameworks.

Daily Report: January 08,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the License UI issue
File "E:\Mime_Backup\2025\January\eBrainVoice_V4pt0 - v1.1 - Current Working\eBrainVoice\module\src\view\LicenseUI.py", line 151, in check_license_status
license_file_path = self.file_entry.get().strip()
^^{^^}^^^^^^^^^^
AttributeError: 'LicenseManager' object has no attribute 'file_entry'

2. Refactor the codebase to improve clarity and maintainability by separating the license management logic and the login functionality into two distinct Python files,license_ui.py and login_ui.py.
3. Both the login and license code are encapsulated within their classes, each class should include relevant methods, with the entry point of the program managed using the standard if name == "__main__": construct for execution.
4. Implement the license activation UI with conditional behavior where the license activation UI opens only if the license is invalid or expired, and otherwise redirects to the loginui.py.

Daily Report: January 09,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving issues in the license module related to date tampering logic and the repeated opening of the UI.
2. Reviewing the document titled "Call for Investigator-Initiated Research Proposals for Small Extramural Grants (2025)."
3. Exploring the Bootstrap front-end framework for web development.
4. Investigating the use of CDN (Content Delivery Network) links for hosting Bootstrap files.
5. Familiarizing with the functionalities and HTML tags implemented for the web version of the MiMe software.

Daily Report: January 10,2025
------------------------------------------------------------------------------------------------------------------------
1. Discussion on the development and deployment of the web-based version of the standalone MindMe application.
2. Troubleshooting the issue where accuracy metrics displayed during the testing of new data are inconsistent when the user interface (UI) is closed and reopened for subsequent testing with a new UI.
3. Implementation of the web version includes adding a toggle dropdown button within the UI component named "Model Creation" as part of the experimental UI, utilizing JavaScript for functionality and HTML tags for structure, with the relevant function defined within the <script> tag.
4. Model training and evaluation are conducted using the CatBoost algorithm, with the code modified to handle an 80-20 split of combined patient volunteer data for training and testing.

Daily Report: January 13,2025
------------------------------------------------------------------------------------------------------------------------
1. Conducted an exploration of disorders of consciousness (DOC) and reviewed the paper titled "Network Dynamics in Disorders of Consciousness: A Graph Theoretical
Perspective."
2. Investigated the interconnection between graph theory and the debiased weighted phase lag index, analyzing their applications in the context of DOC.
3. Reviewed the paper "Advancing Anxiety Detection: Insights from ECG Signal Processing" and prepared a review document with modification to be done in the paper.
4. Resolved an issue where accuracy metrics displayed during the testing of new data became inconsistent when the user interface (UI) was closed.
5. Performed training and testing using an 80-20 data split and verified the accuracy consistency with newly introduced datasets.

Daily Report: January 15,2025
------------------------------------------------------------------------------------------------------------------------
1. License module is successfully integrated with the updated version of eBrainVoice_V4pt0.
2. Exploring the bootstrap front end framework - Containers, Grid System basics - for the development of web version of MindMe.
3. Testing is performed with random forest classifier with 20% split data and new data.
4. Discussion with Vaishnavi regarding the workflow of license module for the development of .exe.

Daily Report: January 16,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring the bootstrap framework - how to implement tables, different types of tables, different layouts of uploading a image, Page headers.
2. Conducted an exploration about the implementation of "Jumbotron" using bootstrap.
3. Discussion on the various web development topics, sample ui designs, for the development of web version of MiMe software.
4. Testing is performed with random forest classifier while closing the UI after each testing to test whether issue is solved or not.

Daily Report: January 17,2025
------------------------------------------------------------------------------------------------------------------------
1. Prepared the PPT for the CAR Proposal includes rationale, objectives ,output, methodology, objectives and timelines.
2. Exploring the bootstrap framework - bootstrap wells, types of alerts, button implementation, button grouping, split button dropdowns, badges and labels.
3. Exploring the "Glyphicons" in bootstrap and different types of glyphicons and its implementation.
4. Attended the discussion on the working principle of EEG signals and how the electrodes collect the signal from brain etc.

Daily Report: January 21,2025
------------------------------------------------------------------------------------------------------------------------
1. Participated in a discussion on web development, covering topics such as Bootstrap, REST API, and ReactJS etc.
2. Conducted training and testing of the Random Forest model to document its accuracy.
3. Investigating the integration of Bootstrap with ReactJS for developing a web-based application.
4. Exploring templates that integrate Bootstrap, ReactJS, and Django for web development.

Daily Report: January 22,2025
------------------------------------------------------------------------------------------------------------------------
1. Testing the model created using random forest classifier and documenting the accuracy in excelsheet.
2. Exploring the "Integrating Django with Reactjs using Django REST Framework" for the web development.
3. Exploring the hyper parameters tunning for various models.
4. Analyzing a Bootstrap-Django based theme for the web development.

Daily Report: January 23,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring the process of communicating between the Django Backend and Reactjs frontend using the Django REST Framework.
2. Exploring into a UI of the task manager application is a sort of to-do list as an example of Django with Reactjs using Django REST Framework.
3. Testing the trained model using random forest with new dataset is carried out and documented the accuracy in excelsheet.

Daily Report: January 24,2025
------------------------------------------------------------------------------------------------------------------------
1. Explained the code of licensing module and related modules and how the encryption works in the key generation tool.

Daily Report: January 27,2025
------------------------------------------------------------------------------------------------------------------------
1. Attended the discussion regarding the web development, Django-REST framework , folder structure, CSRF attack.
2. Explored the architecture of Django backend framework for understanding how its works.

Daily Report: January 28,2025
------------------------------------------------------------------------------------------------------------------------
1. Participated in a meeting regarding the "Remote Monitoring System for Alcohol Addiction Treatment."
2. Conducting an exploration of hyperparameters for the Random Forest classifier and implementing their configuration.
3. Training the Random Forest model with n_estimators=200 to evaluate its impact on accuracy.
4. Developing code to retrieve all MAC addresses from the system for validating the license MAC address.

Daily Report: January 29,2025
------------------------------------------------------------------------------------------------------------------------
1. Conducted testing on the Random Forest classifier with n_estimators set to 200 to evaluate its impact on model accuracy and performance improvements.
2. Explored research papers relevant to the development of the project proposal "Emotion Monitoring System for Post-Deaddiction Patients to Assess Substance Cravings".
3. Investigated advanced EEG-based learning frameworks, including "BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data" and "Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition," to understand their applications in EEG signal processing and feature extraction.
4. Identified and successfully resolved an issue related to fetching the MAC address during the activation of the licensing system.
5. Analyzed the source code to enable the addition and configuration of hyperparameters within the user interface (UI), enhancing model flexibility and user control over training parameters.

Daily Report: January 30,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring and understanding the how to access the super computing facility.
2. Exploring the PARAM Siddhi-AI User Manual.
3. Conducted a training for creating random forest model with n estimator 500 for evaluating the accuracy.
4. Implementing a option with checkbox to browse the file for retraining the model in the MiME v1_pt0.

Daily Report: January 31,2025
------------------------------------------------------------------------------------------------------------------------
1. Developing an entry field to browse and select pre-trained models with .sav extension for retraining.
2. Integrating checkRetrainClicked method to enable file selection for retraining.
3. Exploring the retraining workflow to facilitate model updates with new data.
4. Developing and implemented retrain_model function to execute the retraining process.

Daily Report: February 03,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementation of the retraining feature in the MindMe software to update and enhance trained models.
2. Training and testing conducted to analyze the performance of the retrained model using the InnerSpeech dataset.
3. Familiarization with the web-based code structure of the MindMe Standalone software.

Daily Report: February 04,2025
------------------------------------------------------------------------------------------------------------------------
1. Retrained the Random Forest model, initially trained on 80% of the InnerSpeech dataset, using new data to enable continuous learning and enhance accuracy improvement.
2. Evaluating the retrained model using the remaining 20% of the dataset to validate the effectiveness of retraining.
3. Designing the retraining option UI, ensuring proper alignment without overlapping with the Save Model field.
4. Investigating methods for comparing the trained and retrained models to assess improvements in performance.
5. Exploring techniques for saving models in different file formats.

Daily Report: February 05,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing Retraining Option in the Popup, Adding functionality to the popup for entering the path to the model that will be retrained.
2. Integrated a dropdown menu to select the file extension format (e.g., .sav, .pkl, .json, .h5) for saving the retrained model.
3. Adjusting the layout of the popup and UI elements to ensure that no other design elements or options are displaced or overlapping.
4. Upsampling and Organizing Data from Indices 140-161 for testing of the retrained model.

Daily Report: February 06,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing an extension function for the model to save files with different extensions and modify the file structure accordingly.
2. Developing a model to test the functionality of a progress bar in the Live UI using one hour of seizure data.
3. Added a save button to the popup that stores the retraining path and file extension in a JSON file.
4. Implementing a retraining function that retrieves the path from the popup and retrains the model.

Daily Report: February 07,2025
------------------------------------------------------------------------------------------------------------------------
1. GMCT visit for seeing how the eeg is recorded.
2. Successfully generated a randomforest model with seizure dataset for testing the Live UI.
3. Testing the model in the Live UI shows an error "wavelet = wavelet_json["Wavelets"]["SelectedWavelet"]
~_~~~~~~~~~^^^^^^^^^^^^
KeyError: 'Wavelets'" with 1 hour seizure data of patient id 23.
4. Testing the generated random forest model with seizure dataset of patient 23.

Daily Report: February 08,2025
------------------------------------------------------------------------------------------------------------------------
1. Generating a CAR proposal on the topic "Emotion Monitoring System for Post-Deaddiction Patients to Assess Substance Cravings".
2. Troubleshooting the issue in the liveui "Process finished with exit code -1073740791 (0xC0000409)".
3. Testing the random forest model trained using seizure data of patient id 23.
4. Implementing the retrain model option and troubleshooting the issues in integration.

Daily Report: February 10,2025
------------------------------------------------------------------------------------------------------------------------
1. Training the random forest classifier using time domain features with seizure data for evaluating the accuracy of the model.
2. Implementing the retrain function of ML and DL including to the file extension function.
3. Tracing the count of the files of each subject for training and testing.
4. Attended the meeting regarding the deployment of seizure prediction system in GMCT

Daily Report: February 11,2025
------------------------------------------------------------------------------------------------------------------------
1. Discussion regarding the procedures for training and testing , dataset exploration, with Vaishnavi for seizure prediction .
2. Training the random forest model using features like skewness, kutrosis, PLV, decorrelation time for attaining better model.
3. Testing the model using 20% splitted data, new data from subjects 24,25,26,27 attained 49.5% accuracy for new data for the subject 26.
4. Integrating the progress bar code with current updated version of LiveUI.

Daily Report: February 12,2025
------------------------------------------------------------------------------------------------------------------------
1.Troubleshooting the error when running the latest version od LiveUi software " File "E:\svn repository\live_ui\execute\executePipeline.py", line 25, in <module>
from eBrainVoice.module.execute.event_selection import event_selection, check_event_list_selected
ModuleNotFoundError: No module named 'eBrainVoice'"
2. Integrating and testing the live ui for the functioning of progress bar in the LiveUI.
3. Identifying the issue in the plotting of progress bar in the Live UI.

Daily Report: February 13,2025
------------------------------------------------------------------------------------------------------------------------
1. Successfully integrating the live UI code with progress bar in the new updated version of the LiveUI.
2. Identifying the issue why the event is not displayed in the progress bar.
3. Training the random forest model with inner speech patient & volunteer combined dataset using the bandpass filter and wavelet features.
4. Testing the new seizure dataset not included in the training for the seizure prediction using random forest classifier.

Daily Report: February 17,2025
------------------------------------------------------------------------------------------------------------------------
1. Testing the EEGNET classifier to evaluate the accuracy using deep learning models with inner speech dataset.
2. Troubleshooting the error when testing the EEGNET model with inner speech dataset
File "E:\svn repository\MiME_V1pt0\eBrainVoice\module\src\View\EbrainVoicePipeline.py", line 2342, in execute_button_activated
with open(saving_path, 'w', encoding='utf-8') as f:
^^{^^}^^^^^^
UnboundLocalError: cannot access local variable 'saving_path' where it is not associated with a value
3. Training an EEGNET model using the innerspeech dataset without preprocessing and features using MiME_V1pt0.
4. Testing the random forest model with entirely new dataset of inner speech not included in the training.

Daily Report: February 18,2025
------------------------------------------------------------------------------------------------------------------------
1. Encountered an error while testing the 1 hour data of seizure "File "E:\svn repository\MiME_V1pt0\eBrainVoice\module\execute\executePipeline.py", line 363, in execute_pplfunc
if int(self.class_name) 29 or int(self.class_name) 30:
^^{^^}^^^^^^^^^^^^^^^
ValueError: invalid literal for int() with base 10: '23.ASC'"
2. Exploring the code for implementing the progress bar and subplots in the MiME_V1pt0.

Daily Report: February 19,2025
------------------------------------------------------------------------------------------------------------------------
1. Designing the UI for enabling the option to see EEG in eegviewer, view progress bar, field to enter the total data duration.
2. Implementing the function to perform when the checkbox is enabled for progress bar and EEG display.
3. Testing the random forest model with 1 hour data and encountered an error "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py", line 2829, in _check_n_features
raise ValueError(
ValueError: X has 18514 features, but RandomForestClassifier is expecting 16 features as input.
"
4. Training a random forest model with 23 subject id with 48 channels and encountered issue "raise IndexError("single positional indexer is out-of-bounds")
IndexError: single positional indexer is out-of-bounds"
5. Attended the lecture series of iCON BCI on the topic "Brain Anatomy".

Daily Report: February 20,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the function to view the eeg viewer when the display eeg checkbox is enabled.
2. Created a model using seizure dataset with 16 channels for all subject using random forest classifier.
3. Tested the created random forest model using the trained data and got the 100% accuracy.
4. Testing the model using 20 % new dataset and encountered the error " File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py", line 2829, in _check_n_features
raise ValueError(
ValueError: X has 20 features, but RandomForestClassifier is expecting 16 features as input."

Daily Report: February 21,2025
------------------------------------------------------------------------------------------------------------------------
1. Testing the 1 hour seizure data of subject 23 with 48 channels and encountered error "raise ValueError(
ValueError: X has 9246 features, but RandomForestClassifier is expecting 8 features as input."
2. Attended the meeting of the demo presentation of inner speech using Mime software.
3. Evaluated the no of epochs in each file and total count of epochs in each event and reduced it to 96 for ensuring the balanced inner speech data.
4. Training the model using the balanced data using random forest classifier for evaluating the accuracy of each event.
5. Exploring the concept for testing the 1 hour data using Mime software by implementing a function to read the data.

Daily Report: February 24,2025
------------------------------------------------------------------------------------------------------------------------
1. Implemented a model of random forest using inner speech dataset and get the training accuracy of 99.77% and test accuracy for 20% data is 57.23%.
2. Tested each event using random forest model and got the accuracy for each event 2-62.5%, 7-66.66%, 8-62.5%, 10-52.94%, 12-50%.
3. Troubleshooting the issue "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py", line 2829, in _check_n_features
raise ValueError(
ValueError: X has 18514 features, but RandomForestClassifier is expecting 16 features as input."
4. Exploring the mechanism to process a 1-hour EEG file without loading the entire file into memory.

Daily Report: February 25,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring various libraries to read long EEG data in epochs based on user-defined duration and epoch length without loading entire file.
2. Implementing a function "def long_eeg_processing_function(epoch_duration,sample_rate)" processes EEG data by dividing it into epochs based on the given epoch_duration and sample_rate.

Daily Report: February 26,2025
------------------------------------------------------------------------------------------------------------------------
1. Explored the NumPy library for reading the long EEG data.
2. Implemented the function to read the long EEG data as chunks and store the chunk in the nparray.
3. Troubleshooting an error encountered during the chunk reading "LoadedSignal <generator object process_large_asc_file at 0x000001EA2D417350>".

Daily Report: February 27,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing function to read the long EEG data
2. Implementing the logic for merges the last smaller chunk with part of the previous chunk, ensures all processed chunks have 1600 samples.
3. Troubleshooting the issue that only the last chunk is sent for feature extraction.

Daily Report: February 28,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring how to calculate the no of epochs, file duration of a long EEG file without reading the file.
2. Exploring how to read the long EEG edf files as chunks.
3. Designing the Mime UI to create the entry field for epoch duration and total duration in the configuration settings in testing.

Daily Report: March 03,2025
------------------------------------------------------------------------------------------------------------------------
1. Attended the meeting regarding various machine learning classifers.
2. Implemented the code for spam email detection using Naive Bayes classifier in pycharm.
3. Implementing the retrain option in the Mime software.

Daily Report: March 04,2025
------------------------------------------------------------------------------------------------------------------------
1. Creating a CAR proposal on the topic "Emotion Monitoring System for Post-Deaddiction Patients to Assess Substance Cravings".
2. Designing an architecture for the proposal.
3. Exploring the methodology for implementing the emotion monitoring system.
4. Exploring the papers related to the "Emotion Monitoring System for Post-Deaddiction Patients".

Daily Report: March 05,2025
------------------------------------------------------------------------------------------------------------------------
1. Preparing the proposal document, including methodology and game development.
2. Exploring emotional diversion games for post-addiction recovery patients.
3. Designing the architecture with game development.
4. Attended a meeting related to the regression presentation.

Daily Report: March 06,2025
------------------------------------------------------------------------------------------------------------------------
1. Solved the alignment issue of the preprocessing check button in the image generation UI.
2. Explored the code for implementing image generation includes the spectrogram generation.
3. Generated the multichannel scalogram images of seizure.
4. Implementing the long EEG processing without loading into the memory.
5. Troubleshooted the issue in generating the spectrogram in image generation.

Daily Report: March 07,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the modification in design of Mime UI as version MiME_V2pt0.
2. Designing the model execution UI for testing the created model.
3. Implementing the function to display the model execution UI in the top when browse button is clicked.
4. Designing the popup when each button in the is clicked.

Daily Report: March 08,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the popup when the button of load data, feature extraction, AI, preprocessing.
2. Designing the pages with corresponding function when the button is clicked.
3. Implementing image data generation ui in the MiME_V2pt0.

Daily Report: March 10,2025
------------------------------------------------------------------------------------------------------------------------
1. Attended the meeting about CART algorithm, decision tree, KNN.
2. Explained the program logic how the naive bayes algorithm works with email.csv dataset.
3. Modified the PPT of Naive Bayes algorithm.
4. Implementing the ML/DL UI to select different classifiers for the model creation.

Daily Report: March 11,2025
------------------------------------------------------------------------------------------------------------------------
1. Attended the demo meeting of the MiME InnerSpeech Software presentation for Techverse.
2. Implemented the AI component in the UI to allow users to choose between an ML or DL classifier.
3. Developed the UI for model creation using various classifiers.
4. Designed the model creation UI, adjusting the background and foreground components to match the parent UI.
5. Troubleshot the issue where clicking the button resulted in no response or UI change.

Daily Report: March 12,2025
------------------------------------------------------------------------------------------------------------------------
1. Testing the MiME_V2pt0 software to ensure successful model creation and proper functioning of all features.
2. Designing the post-processing UI for model execution in MiME_V2pt0 software.
3. Implementing functions to display results and provide a "Write to Disk" option in the UI.
4. Establishing the connection to invoke functions using the radio button option.
5. Troubleshooting the error occurred while implementing the Post Processing UI "File "E:\svn repository\MiME_V2pt0\eBrainVoice\module\src\View\PostProcessingUI.py", line 353, in save_result_path
self.gSaveResultPath = filedialog.askdirectory()
^^{^^}^^^^^
NameError: name 'filedialog' is not defined"

Daily Report: March 19,2025
------------------------------------------------------------------------------------------------------------------------
1. Implemented TrainingUI for selecting the classifier type (ML or DL) and regressor.
2. Added a browsing option in the AI module for selecting the model path for training.
3. Modified the model execution process by removing buttons and highlighting labels.
4. Resolved the issue with loading the MIME logo in the UI.

Daily Report: March 20,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooted and resolved the issues in the training UI while choosing the ML or DL classifier.
2. Resolved the issue in viewing the model browsing field with adapt to the others frames.
3. Troubleshooted the issue in saving the model to the folder, when the model browse option added in the AI module.
4. Successfully trained the model using the classifier for verifying all the modules works well.
5. Resolved the issues in the post processing module while clicking the radio buttons.

Daily Report: March 21,2025
------------------------------------------------------------------------------------------------------------------------
1. Trained the Random Forest model using an 80% balanced dataset of inner speech data with MiME_V2.0 software.
2. Organized the dataset for Volunteers 1 and 2 by categorizing their data into appropriately labeled folders.
3. Upsampled the inner speech data of Volunteers 1 and 2 to a sampling rate of 4096 Hz for model training.
4. Trained the Random Forest model using the upsampled combined dataset of Volunteer 1 and 2, as well as Volunteer 1 data separately.
5. Fixed the training UI bugs, including issues in Deep Learning fields population.
6. Validating the model execution by ensuring that the entry field is not empty when executing the testing process.
7. Attended the icon BCI lecture series part 5 on the concept of deep learning.
8. Prepared a workflow of the MiMe software with the UI.

Daily Report: March 22,2025
------------------------------------------------------------------------------------------------------------------------
1. Conducted testing using the Random Forest model in MiMe Version 2 and identified the issue: "BoundLocalError: cannot access local variable 'result_individual_mode_merged_predict' where it is not associated with a value."
2. Resolved the button overlapping issue in the Post-Processing UI when the "Write" and "Display" checkboxes are selected.
3. Implemented validation checks in the Model Execution module to ensure that all required entry fields are filled before execution.
4. Fixed an issue where the Model Execution window was being destroyed when an validation popup appeared. The message is now displayed properly within the Model Execution UI without closing the model execution window.
5. Developed a customizable popup with configurable background color, font, button styles, and icons to align with the UI design.

Daily Report: March 24,2025
------------------------------------------------------------------------------------------------------------------------
1. Added the Naive Bayes description as notes in the presentation.
2. Created a presentation with the comparison of accuracy of random forest and catboost.
3. Documenting the modifications in the proposal "Emotion Monitoring System for Post-Deaddiction Patients to Assess Substance Cravings".
4. Implementing the machine learning and EEGNet algorithms in the laptop for the presentation.
5. Testing MiMe version 2 software using random forest classifier for verifying the functionalities work well.

Daily Report: March 28,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing a progress bar in the parent window of MiME_V2pt0 software which shows the phase of model creation happening in the background.
2. Created a function "create_log_tracker_ui" to show the progress in the progress bar.

Daily Report: April 01,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the progress bar when the model creation button clicked and processes happening in the background.
2. Exploring different types of progress bar for the UI.
3. Troubleshooting the error "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\tkinter\__init__.py", line 1948, in call
return self.func(*args)
^^{^^}^^^^^^^^^^^
File "E:\svn repository\MiME_V2pt0\eBrainVoice\module\src\View\EbrainVoicePipeline.py", line 302, in <lambda>
padx=10, pady=10, command=lambda: execute_button_activated(update_progress))
^^{^^}^^^^^^^^^^
NameError: name 'update_progress' is not defined"
4. Resolved the isue while running the UI with the error "TypeError: execute_button_activated() missing 1 required positional argument: 'update_progress'"

Daily Report: April 02,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing a class "class CircularProgressBar" for displaying a circular progress bar in the Main UI of the software.
2. Developed a draw_circle function of showing the percentage in the circular progress bar.
3. Resolving the alignment issue and the color of the circular progress bar with compatible to the software.
4. Implementing a thread to access the global variable in each functionality of the software.

Daily Report: April 03,2025
------------------------------------------------------------------------------------------------------------------------
1. Implemented and starts a new thread to execute the function update_task_progress().
2. Implementing file processing script that loads files while updating a progress status displaying background data loading without freezing the main application.
3. Resolved the issue in the placement, center the progress bar horizontally while keeping it near the bottom.
4. Troubleshooting the issue that the circular progress bar is not appears properly.

Daily Report: April 04,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the circular progress bar move forward from 0% to 100% based on how many status updates are received.
2. Modifying the circular progress bar to complete a full 360-degree arc upon reaching 100% progress.
3. Exploring the ways to to draw a smooth, high-quality circular progress bar with well-finished edges.
4. Troubleshooting the issue that the thread is not continuously update a Tkinter Label in a background task.
5. Attended the icon BCI lecture series part 7 on the concept of "Ionic signaling and processing in neurons".

Daily Report: April 05,2025
------------------------------------------------------------------------------------------------------------------------
1. Implemented dynamic label updates within the progress bar to indicate the current background process.
2. Resolved the issue where progress labels were only updating at the end of the execution instead of during intermediate steps.
3. Debugged and fixed the error: TypeError: ClassifierTrain.__init__() takes 2 positional arguments but 3 were given by modifying the class constructor appropriately.
4. Resolving the error: TypeError: ClassifierTrain.classfr_json() missing 1 required positional argument: 'label' by updating the method signature and call to handle class attributes correctly.
5. Attended a meeting about architecture of Big Data and Hadoop.

Daily Report: April 06,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the error "ile "E:\svn repository\MiME_V2pt0\eBrainVoice\module\execute\executePipeline.py", line 889, in execute_pplfunc
ClassifierTrain(all_feature_all_file_merged,label)TypeError: ClassifierTrain.__init__() takes 2 positional arguments but 3 were given".
2. Redesigned the progress bar to smoothly animated the circular progress and visually matched labels.
3. Successfully implemented the circular progress bar in the main UI when the model creation is performed.
4. Implementing the function to disappear the progress bar from the UI when the bar reaches 100% and accuracy is displayed.

Daily Report: April 07,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the error "ile "E:\svn repository\MiME_V2pt0\eBrainVoice\module\execute\executePipeline.py", line 889, in execute_pplfunc
ClassifierTrain(all_feature_all_file_merged,label)TypeError: ClassifierTrain.__init__() takes 2 positional arguments but 3 were given".
2. Redesigned the progress bar to smoothly animated the circular progress and visually matched labels.
3. Successfully implemented the circular progress bar in the main UI when the model creation is performed.
4. Implementing the function to disappear the progress bar from the UI when the bar reaches 100% and accuracy is displayed.

Daily Report: April 07,2025
------------------------------------------------------------------------------------------------------------------------
1. Implemented the condition to disappear the circular progress bar when the bar reaches 100%.
2. Implementing the circular progress bar when the execute button clicked on the model execution UI.
3. Troubleshooting the error "UnboundLocalError: cannot access local variable 'result_individual_mode_merged_predict' where it is not associated with a value".
4. Integrating the circular progress bar code with the updated version of the software.

Daily Report: April 08,2025
------------------------------------------------------------------------------------------------------------------------
1. Implemented the condition to disappear the circular progress bar when the bar reaches 100%.
2. Implementing the circular progress bar when the execute button clicked on the model execution UI.
3. Troubleshooting the error "UnboundLocalError: cannot access local variable 'result_individual_mode_merged_predict' where it is not associated with a value".
4. Integrating the circular progress bar code with the updated version of the software.

Daily Report: April 09,2025
------------------------------------------------------------------------------------------------------------------------
1. Encountered error when integrating the circular progress bar code with the software "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\tkinter\__init__.py", line 3109, in get
return self.tk.call(self._w, 'get')
^^{^^}^^^^^^^^^^^^^^^^^^^^^^^
_tkinter.TclError: invalid command name ".!labelframe2.!entry2""
2. Troubleshooting the error "tkinter.TclError: can't invoke "winfo" command: application has been destroyed".
3. Attended the icon BCI lecture series part 8 on the topic "EEG based biometric authentication and identification".

Daily Report: April 11,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the circular progress bar is on progress in the model creation UI.
2. Troubleshooting the issue faced while the integration of the circular progress bar code "File "E:\svn repository\MiME_V2pt0\eBrainVoice\module\src\ai\ml\ClassifierTrain.py", line 91, in classfr_json
self.test_size = float(v)
^^{^^}^^^
TypeError: float() argument must be a string or a real number, not 'NoneType'".

Daily Report: April 15,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the errors while integrating circular progressbar in the model creation UI
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
AttributeError: 'NoneType' object has no attribute 'endswith'
TypeError: the JSON object must be str, bytes or bytearray, not dict etc...
2. Successfully integrated the circular progressbar in the model creation UI of the updated software.
3. Implementing the progress bar in the model execution UI.
4. Troubleshooting the error "UnboundLocalError: cannot access local variable 'result_individual_mode_merged_predict' where it is not associated with a value"

Daily Report: April 16,2025
------------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the errors occurred in the model creation
"1. tkinter.TclError: invalid command name ".!labelframe2.!entry3"

2. tkinter.TclError: can't invoke "winfo" command: application has been destroyed

3. File "C:\Users\hp\AppData\Local\Programs\Python\Python311\Lib\json\__init__.py", line 339, in loads
raise TypeError(f'the JSON object must be str, bytes or bytearray, '
TypeError: the JSON object must be str, bytes or bytearray, not dict"
2. Setting demo of the MiMe Version 2 software in the laptop.
3. Training the model with innerspeech balanced epochs dataset using SVM classifier.
4. Training the model using large dataset leads the UI to not responding state.

Daily Report: April 17,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the issues " if int(self.class_name) 29 or int(self.class_name) 30:
^^{^^}^^^^^^^^^^^^^^^
ValueError: invalid literal for int() with base 10: 'software'

IndexError: index 21 is out of bounds for axis 1 with size 21"
2. Meeting with Subodh sir regarding the implementation of licensing in the software.
3. Attended the icon BCI lecture series part 9 on the concept of "The utility of whole person cognition for BCI".

Daily Report: April 21,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementation of licensing with the generation of a serial number using the MAC address.
2. Exploring methods to access the last 4 digits of the MAC address without user input.
3. Implementing a UI for user registration and serial number generation.
4. The requirements document has been reviewed, and the necessary modifications have been incorporated.

Daily Report: April 22,2025
------------------------------------------------------------------------------------------------------------------------
1. Added two labeled entry fields for entering the email ID and phone number during registration.
2. Created a popup window to display the serial number along with a registration success message.
3. Replaced the default message box with a custom Toplevel window styled using ttkbootstrap.
4. Implemented a register() function that includes a “Copy” button in the popup, allowing users to copy the serial number to the clipboard.
5. Resolved the error "AttributeError: 'RegistrationApp' object has no attribute 'tip_window'bounds for axis 1 with size 21"

Daily Report: April 23,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring the code for getting the MAC address from the device.
2. Implementing the PyQt5-based UI for generating the device serial number automatically from the MAC address.
3. Resolved the issue when the UI became unresponsive when clicking the "Detect Device" button.
4. Troubleshooting the issue that the code is getting stuck after sending the handshake ("CCET-ACK\n") to the Arduino, likely during the part where it waits for a response.

Daily Report: April 24,2025
------------------------------------------------------------------------------------------------------------------------
1. Debugging the code to identify issues occurring after the handshake is sent to the Arduino.
2. Implementing the decryption logic to decode the encrypted MAC ID received from the device."
3. Attended the icon BCI lecture series part 9 on the concept of "Signal Processing and Machine Learning for Brain Computer Interfaces Part-2"

Daily Report: April 25,2025
------------------------------------------------------------------------------------------------------------------------
1. Integrating the ChaCha20 decryption into the existing code for decrypting the MAC ID.
2. Modifying the run method to call the decrypt_data function with the encrypted MAC ID string received from the Arduino.
3. Resolved the error "from Crypto.Cipher import ChaCha20 ModuleNotFoundError: No module named 'Crypto'".
4. Debugged whether CCET-ACK is actually sent to the Arduino and what is receieving from the arduino.

Daily Report: April 28,2025
------------------------------------------------------------------------------------------------------------------------
1. Adding a copy button near to product id for easily copying the number.
2. Resolved the error "AttributeError: 'ArduinoProductIDGenerator' object has no attribute 'copy_button'".
3. Added the logo smiriti_Logo.png above the "Product ID Generator" title and position it above the header label.
4. Implementing a license key generator UI for generating license key from the serial number.
5. Training is performed using SVM classifier with inner speech dataset.

Daily Report: April 29,2025
------------------------------------------------------------------------------------------------------------------------
1. Training the SVM models using the large seizure dataset.
2. Implementing the license key generator UI for generating the license key.
3. Implementing the functionality to decrypt the serial number using ChaCha20 and XOR the decrypted bytes to show as License Key.
4. Integrated a loading spinner in the UI, works when the button is clicked.
5. Integrating the SQLite database with the program for saving the serial numbers and license key.

Daily Report: April 30,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing a UI for entering and validating a license key using a 32-character hexadecimal string as the license key.
2. Redesign the entry box with line-style input field instead of a box, with no borders or background.
3. Implementing a function validate_license to decrypt the encrypted license key before validation.
4. Validating the last 4 characters after the first hyphen in the decrypted license key match the last 4 characters of the system's MAC address.
5. Testing is performed using the SVM classifier with seizure dataset.

Daily Report: May 01,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the issue that sometimes the function returns a random MAC or the wrong interface's MAC.
2. Integrating the logic of validating the decrypted license key against the MAC address of the PC.
3. Added a new input field (self.expiry_input) to collect the expiry date in the license key generator UI.
4. Resolving the error "failed to generate license key table license_records has no column named expiry date".
5. Attended the icon BCI lecture series part 11 on the concept of "How attentive you are - an EEG based analysis".

Daily Report: May 02,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing a license activation code that integrates device MAC ID decryption using ChaCha20.
2. Validating the decrypted license key based on the PC mac, device mac and expiry date.
3. Debugging the code to identify the issue that the mac is not getting from the device.
4. Troubleshooting the "Error: MAC + date string too short. Expected at least 13 characters".
5. Adding a check that compares the system date to a last known valid date to detect date tampering
6. Exploring the ways to hide last_date.txt makes it harder for users to manipulate the file and bypass the date-tampering check.

Daily Report: May 05,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring the serial numbers format for license key generation.
2. Reformatting the license key from the serial number.
3. Integrating the circular progress bar in the latest update MiMe software.
4. Resolving error "TypeError: ExecutePipeline.execute_pplfunc() missing 2 required positional arguments: 'label' and 'progress_bar'".
5. Troubleshooting the error "result_individual_file_all_feature_merged_row_wise = self.result_individual_file_all_feature_merged['predictions'].astype(int)
~_~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
TypeError: 'NoneType' object is not subscriptable".

Daily Report: May 07,2025
------------------------------------------------------------------------------------------------------------------------
1. Training the SVM classifier with innerspeech dataset for the generation of best models.
2. Testing the SVM classifier with the trained dataset of innerspeech.
3. Encountered an error while testing the model "result_individual_mode_merged = pd.DataFrame({'Test file name': [self.test_file_name], 'predictions': result_individual_mode_merged_predict})
UnboundLocalError: cannot access local variable 'result_individual_mode_merged_predict' where it is not associated with a value".
4. Implementing logging in the license UI for storing log messages with timestamps and additional information into a file.
5. Attended the icon BCI lecture series part 12 on the concept of "Brain and machine interface and its advanced technology applications"

Daily Report: May 08,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the code to store the last verified date in the system's registry instead of a file.
2. Identified the location where LicenseActivation file is saved in the registry and verified the last valid date.
3. Successfully implemented the logging and registry in the license UI.
4. Training the SVM classifier using 80% innerspeech dataset splitted for training the model.
5. Adding headers and comments for all .py files in the license key generation.

Daily Report: May 13,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the issues that the UI freezing ("not responding") during license activation and delayed popup.
2. Implementing a separate thread using QThread for UI communication without blocking main thread.
3. Added a spinner during validation, displayed on top of the UI when the validation is running.
4. Added an animated "Validating..." label with the loading spinner GIF animation during validation.

Daily Report: May 14,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the error "File "E:\svn repository\KeyGenerator_V1pt0\LicenseGeneration\Rough.py", line 197, in init_ui
self.spinner.setScaledSize(Qt.QSize(24, 24))
AttributeError: type object 'Qt' has no attribute 'QSize'".
2. Exploring the ways to auto-check the license on startup, once the license is activated and stored.
3. Implementing the function to storing the license key locally in the registry.
4. Implementing the function to detect PC MAC change and invalidate the License.
5. Troubleshooting the error "code runs its exit with "Process finished with exit code -1073740791 (0xC0000409)"" related to memory corruption.

Daily Report: May 16,2025
------------------------------------------------------------------------------------------------------------------------
1. Modifying the on_validation_finished function in order to show the UI with message when the license has expired.
2. Exploring the implementation of the spinner when the execute and create model button clicked.
3. Analyzing the no of bytes in each key generated in the key generator.
4. Exploring the ways to fetch current date from an internet-based trusted time source during license activation.
5. Resolving the issue in the circular progress bar.

Daily Report: May 17,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the issue in the decreasing of the percentage in the circular progress bar.
2. Implementing a update update_progress_bar() in order to prevent the progress don't decrease the progress value.
3. Troubleshooting the issue "File "E:\svn repository\MiME_V2pt0\eBrainVoice\module\execute\executePipeline.py", line 86, in execute_pplfunc
self.update_progress_bar(progress_bar, 0)
TypeError: ExecutePipeline.update_progress_bar() missing 1 required positional argument: 'new_progress'"

Daily Report: May 19,2025
------------------------------------------------------------------------------------------------------------------------
1. Created the README document for the hardware serial number generator, license key generator and license activation.
2. Exploring the implementation of the spinner in a UI.
3. Implementing the logic of the spinner by replacing the circular progress bar in the MiME software.

Daily Report: May 20,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the spinner in the MiME software including the labels which shows the current on going process.
2. Troubleshooting the errors -
"1. AttributeError: 'Spinner' object has no attribute 'update_progress'

2. raise IndexError("single positional indexer is out-of-bounds")
IndexError: single positional indexer is out-of-bounds
3. TypeError: execute_button_activated() missing 1 required positional argument: 'spinner'
4. TypeError: Spinner.__init__() missing 1 required positional argument: 'status_label'"
3. Resolving the issue in the stopping and hiding of the spinner when the execution completed.

Daily Report: May 21,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the spinner in the training module and encountered some errors.
2. Resolving the errors -
1. ImportError: cannot import name 'ExecutePipeline' from partially initialized module 'eBrainVoice.module.execute.executePipeline' (most likely due to a circular import) (E:\svn repository\MiME_V2pt0\eBrainVoice\module\execute\executePipeline.py)

2. File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\tkinter\__init__.py", line 2512, in place_forget
self.tk.call('place', 'forget', self._w)
_tkinter.TclError: can't invoke "place" command: application has been destroyed

3. File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\tkinter\__init__.py", line 861, in callit
func(*args)
TypeError: Spinner.stop_animation() missing 1 required positional argument: 'self'

self.master.update_idletasks()
    ^^^^^^^^^
4. AttributeError: 'ClassifierTrain' object has no attribute 'master'

5. self.accuracy=accuracy(self, self.label,self.spinner)
^^{^^}^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: accuracy() missing 1 required positional argument: 'spinner'

Daily Report: May 22,2025
------------------------------------------------------------------------------------------------------------------------
1. Successfully implemented the spinner and labels when the "Create Model" button clicked in the training module.
2. Implementing the spinner and labels in the testing module when the "Execute" button clicked.
3. Troubleshooting the errors when the spinner is implemented in the testing module
1. "UnboundLocalError: cannot access local variable 'result_individual_mode_merged_predict' where it is not
associated with a value"
2. TypeError: Classify() missing 2 required positional arguments: 'test_file_name' and 'label'

4. Successfully implemented the spinner in the testing module.
5. Attended the icon BCI lecture series part 14 on the concept of "Bionic Rehabilitation of Amputees with Brain Signals"

Daily Report: May 23,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the EEG analysis in the live UI for viewing the FFT, offline EEG data and Scalp map.
2. Implementing the "EEG analysis" option in the dropdown and clicking offline gives appropriate options.
3. Troubleshooting the issue "if not hasattr(self, 'data_path_label') or not self.data_path_label.winfo_exists():
AttributeError: 'NoneType' object has no attribute 'winfo_exists'".

Daily Report: May 26,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the function to disappear other frames that are not related with analysis when EEG Analysis is selected.
2. Resolving the error "AttributeError: 'NeurotechAssistApp' object has no attribute 'final_array'".
AttributeError: 'NoneType' object has no attribute 'pack_forget'
3. Implementing a frame to view the offline EEG data in the UI for EEG analysis.
4. Analyzed the innerspeech training and testing dataset for identifying the duplication.

Daily Report: May 27,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing a function for loading the EEG data in different file formats.
2. Implementing the function to automatically identify the channels name from the data itself.
3. Removing the start, stop buttons and upload icon from the UI when the EEG Analysis is selected from the dropdown.

Daily Report: May 28,2025
------------------------------------------------------------------------------------------------------------------------
1. created a new functions to handle the "EEG Analysis" mode with "OFFLINE DATA" selection to plot the data with channels listed on the y-axis.
2. Added a functionality to the UI that x-axis values in the EEG plot update dynamically when the "Next" and "Previous" buttons are clicked.
3. Added a functionality that allows users to change the time window by typing a specific second value in a field between the "Previous" and "Next" buttons.

Daily Report: May 29,2025
------------------------------------------------------------------------------------------------------------------------
1. Implemented the loading of EEG signals in the UI with the channels automatically read from the metadata.
2. Implemented the scalp map of the loaded EEG signals in the UI.
3. Resolved the errors encountered in the generation of scalp map :
1."Error plotting scalp map: plot_topomap() got an unexpected keyword argument 'on_missing
2. Error plotting scalp map: DigMontage is only a subset of info. There are 15 channel positions not present in the DigMontage.
3. Error plotting scalp map: The following electrodes have overlapping positions, which causes problems during visualization:T7, T8, T3, T4".

Daily Report: May 30,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the issue is that the FFT plot in the UI is not displaying correctly.
2. Resolving the issue is that the "Next," "Previous," and "Go" buttons in the UI are not visible and place them between the frames for visibility.
3. Troubleshooting the error encountered while integrating the Next and Previous buttons AttributeError: 'NeurotechAssistApp' object has no attribute 'main_frame'.
4. Attended the icon BCI lecture series part 15 on the concept of "A Framework for EEG Signal Acquisition, Processing, and Application in Neurotechnology".

Daily Report: May 31,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the FFT in the UI on the right side of the EEG signals in the frame.
2. Reposition the "Next," "Previous," "Go" buttons, and the entry field in UI between the EEG data visualization frame and the scalp map frame.
3. Resolving the error " self.tk.call(_flatten((self._w, cmd)) + self._options(cnf))_tkinter.TclError: unknown option "-background""

Daily Report: June 02,2025
------------------------------------------------------------------------------------------------------------------------
1. Modifying the FFT plot in the UI to display the FFT of each EEG channel stacked vertically.
2. Filtering the FFT subplot only the EEG channels FFT will be appeared in the UI.
3. Troubleshooting the error " File "E:\svn repository\MiME_V2pt0\eBrainVoice\module\src\View\neurotechnology_assistive_systemUI.py", line 1452, in plot_eeg_analysis_data
self.lines[k].set_data(times_plotting, IndexError: list index out of range".

Daily Report: June 03,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the errors encountered during the plotting of FFT:
AttributeError: 'NeurotechAssistApp' object has no attribute 'data_settings_frame'
TypeError: len() of unsized object
AttributeError: 'NeurotechAssistApp' object has no attribute 'fft_axs'.
2. Add two checkboxes labeled 'Scalp Map' and 'FFT' to allow users to optionally display the Scalp Map and FFT analysis.
3. Attended the EEG device demo of Aerobe.
4. Resolving the issue when both checkboxes are unchecked, the EEG plot didn't reverts to its original size and layout.
5. Troubleshooting the error when the implementing the option for reverting the EEG signal to its original size "AttributeError: 'NeurotechAssistApp' object has no attribute 'length_of_data'".

Daily Report: June 04,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the error, toggle_fft_visibility method doesn't fully restore the EEG plot to its original size when the FFT checkbox is unchecked.
2. Troubleshooting the error "AttributeError: 'NeurotechAssistApp' object has no attribute 'plot_fft_container'".
3. Implementing the functionality to apply the filters into the EEG plot for EEG analysis.
4. Resolving the error encountered while filters applied to the EEG plot "notch_frq_int = int(notch_frq_str.split()[0])
ValueError: invalid literal for int() with base 10: 'None'
if self.notch_freq > 0: TypeError: '>' not supported between instances of 'NoneType' and 'int'.
ValueError: Digital filter critical frequencies must be 0 < Wn < 1.

Daily Report: June 05,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the functionality to load and analyze the .edf data of meditation.
2. Resolving the error when applying the lowpass, highpass and notch filter in the EEG analysis.
3. Troubleshooting the error "Error applying bandpass filter: Unexpected shape for zi: expected (8,33), found (8,23)".
4. Redesigning the position of the navigation tools in the between the EEG plot and scalp map.

Daily Report: June 06,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the draggable function to drag the scalpmap.
2. Troubleshotting the error " self.tk.call(self._w, 'forget', child)
_tkinter.TclError: .!neurotechassistapp.!frame2.!panedwindow.!labelframe is not managed by .!neurotechassistapp.!frame2.!panedwindow"
3. Modifying the function to read the .edf file for EEG analysis.

Daily Report: June 08,2025
------------------------------------------------------------------------------------------------------------------------
1. Preparing the EEG analysis UI for presenting the demo.
2. Resolving the issue while plotting the .asc data of Rohit Parmar in the EEG plotting.

Daily Report: June 09,2025
------------------------------------------------------------------------------------------------------------------------
1. Analyzing the seizure data for plotting in the EEG Analysis UI.
2. Implementing separate functions for the scalp map and FFT to improve the processing speed of plotting.
3. Creating an inner frame, scalp_map_container, for viewing the scalp map in the center.

Daily Report: June 10,2025
------------------------------------------------------------------------------------------------------------------------
1. Training is performed for the inner speech dataset using CNN_BiLSTM, Random Forest classifiers using various features.
2. Implementing the modification in the placement of the scalp map in the EEG analysis UI.
3. Implementing the draggable function for the scalp map in the UI.

Daily Report: June 11,2025
------------------------------------------------------------------------------------------------------------------------
1. implementing a draggable scalp map frame that resizes dynamically and adjusts the EEG plot size accordingly.
2. Troubleshooting the error "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\tkinter\__init__.py", line 4461, in remove
self.tk.call(self._w, 'forget', child)
_tkinter.TclError: .!neurotechassistapp.!frame2.!panedwindow.!labelframe2 is not managed by .!neurotechassistapp.!frame2.!panedwindow".
self.scalp_frame.bind("<Configure>", self.on_scalp_frame_resize)
^^{^^}^^^^^^^^^^^
AttributeError: 'NeurotechAssistApp' object has no attribute 'scalp_frame'
3. Implementing the batch processing for the data that loaded in the EEG analysis UI.

Daily Report: June 12,2025
------------------------------------------------------------------------------------------------------------------------
1. Integrating the channel selection UI with the EEG analysis UI when the .edf data is loaded.
2. Resolving the error encountered when .asc data is loaded.
3. Training and testing using random forest model with inner speech dataset.
4. Attended the icon BCI lecture series part 17 on the concept of "Applications of Brain-Computer Interfaces in the Diagnosis of Mental Health Disorders".

Daily Report: June 13,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the "Montage error: DigMontage is only a subset of info".
2. Testing is performed using random forest model with entirely new dataset of volunteers.
3. Troubleshooting the "Error plotting scalp map: Number of channels in the Info object (21) and the data array (17) do not match".

Daily Report: June 14,2025
------------------------------------------------------------------------------------------------------------------------
1. Integrating the method for uploading both the .asc and .edf files in the EEG analysis UI.
2. Resolving the "Error plotting scalp map: index 15 is out of bounds for axis 1 with size 15".
3. Testing is performed with random forest model using inner speech dataset of Volunteers 1 and 2.

Daily Report: June 16,2025
------------------------------------------------------------------------------------------------------------------------
1. Resolving the issues in the integration of function for loading edf and asc data in the EEG analysis UI.
2. Training is performed using inner speech dataset includes the volunteer data in the training dataset.
3. Testing is performed with the previous model trained with inner speech dataset exclude the volunteer 1 & 2 data.

Daily Report: June 17,2025
------------------------------------------------------------------------------------------------------------------------
1. Training is performed using the inner speech dataset using patient-volunteer combined data includes volunteer 1 and 2.
2. Created a dataset of volunteer 3 and 4 for testing the random forest model for better accuracy.
3. Using the created random forest model, testing is performed with entirely new data of volunteer 3 and 4.
4. Documented the accuracy and other evaluation metrics in the excel.
5. Testing is performed with the created random forest model in the Live UI.

Daily Report: June 18,2025
------------------------------------------------------------------------------------------------------------------------
1. Created a dataset of patient from 140-161 with the events 2,8,10 for testing.
2. Testing the new patient dataset with the random forest model.
3. Tested each patient dataset from 140 to 161 for the analyzing the better accuracy.

Daily Report: June 19,2025
------------------------------------------------------------------------------------------------------------------------
1. Training is performed with random forest model with processing window 2 and wavelet features.
2. Individual testing is performed with random forest model of entirely new data of patients.
3. Documented the accuracy and values of evaluation metrics in a excel.
4. Exploring the confusion matrix and evaluation metrics.

Daily Report: June 20,2025
------------------------------------------------------------------------------------------------------------------------
1. Explored and documented the evaluation metrics and confusion matrix.
2. Explored which the lobes effectively gives the inner speech for emotiv.
3. Testing is performed using the newly created random forest model with inner speech dataset.
4. Explored how the emotiv works for for live prediction.

Daily Report: June 21,2025
------------------------------------------------------------------------------------------------------------------------
1. Testing is performed with the created random forest model in the live UI.
2. Inner speech recording of 3 events is performed using Emotiv.
3. Created the document of advanced evaluation metrics used for the measuring the model accuracy.

Daily Report: June 23,2025
------------------------------------------------------------------------------------------------------------------------
1. Exploring the contents required for the development of the word document of inner speech.
2. Installation of the MATLAB for the epoch generation is performed.
3. Explored the software of the epoch generation of inner speech data.

Daily Report: June 24,2025
------------------------------------------------------------------------------------------------------------------------
1. Epoch generation is performed for the inner speech dataset of volunteer 3 for 3 events(2,8,10).
2. Created a different random forest models using the dataset for live prediction.
3. Testing is performed with the created random forest model.
4. Live testing with the random forest model is performed.

Daily Report: June 25,2025
------------------------------------------------------------------------------------------------------------------------
1. Prepared the evaluation metrics document outlining performance measures for inner speech detection.
2. Trained the CNN-BiLSTM model using the inner speech EEG dataset.
3. Recorded the live software demonstration for creating the project video.
4. Prepared the script for the voice-over to accompany the demonstration video.

Daily Report: June 26,2025
------------------------------------------------------------------------------------------------------------------------
1. Implementing the spinner in the deep learning classifier function.
2. Implementing the label "Classification is performing" in the classify method of the ClassifierTrainDL.
3. Integrated the function for catching the errors in the spinner.

Daily Report: June 27,2025
------------------------------------------------------------------------------------------------------------------------
1. Reviewing the proposal titled "Development and validation of an Indigenous digital wearable bio-signal detection system to detect craving for alcohol".
2. Prepared the ToT titled "Proposal for ‘Inner Speech and EEG Analyzer’ product Technology Transfer and for finalizing licensing terms".
3. Resolving the error of keras library in the classifyDL function.
4. Troubleshooting the missing argument error in the classify function.

Daily Report: June 30,2025
------------------------------------------------------------------------------------------------------------------------
1. Training the random forest model with inner speech dataset with processing window=1 and decision window 3.
2. Testing the data with created random forest model.
3. Implementing the spinner in the deep learning functions and error occurred.
4. Resolving the keras package issue in the classifyTrainDL function "from tensorflow.keras import layers
ModuleNotFoundError: No module named 'tensorflow.keras'".

Daily Report: July 01,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving TensorFlow version issue with the interpreter.
2. Troubleshooting the issue " An error occurred during pipeline execution: type object 'ExecutePipeline' has no attribute 'label'".
3. Testing the random forest model with the volunteer dataset.
4. Resolving the error "[Error] An error occurred during pipeline execution: 'ClassifierTrainDL' object has no attribute 'label'".
5. Resolving the error "An error occurred during pipeline execution: ClassifierTrainDL.__init__() takes 5 positional arguments but 7 were given".
6. Prepared the statistical report of the neural disorders.
7. Created a flow chart for the proposal.

Daily Report: July 02,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving the error "ClassifierTrainDL.classfr_json() missing 1 required positional argument: 'label'
predict() missing 2 required positional arguments: 'spinner' and 'label'".
2. Implemented the spinner in the deep learning classifier functions.
3. Exploring the login UI functions for the implementation of licensing in the MiME software.

Daily Report: July 03,2025
----------------------------------------------------------------------------------------------------------------------
1. Modified the login UI to launch in full screen mode and keep the login form visible in a fixed position.
2. Added a serial status widget below the login form using CTkLabel and CTkButton.
3. Implemented a method to fetch the PC's MAC address encrypt it using ChaCha20.
4. Diagnosed and resolved the ModuleNotFoundError: No module named 'Crypto' and installed pycryptodome in the correct interpreter..

Daily Report: July 04,2025
----------------------------------------------------------------------------------------------------------------------
1. Positioned and resized right_frame and serial_frame with equal widths and proper alignment using place() and anchor='w'.
2. Applied visual styling to the serial frame for better layout debugging.
3. Displayed ChaCha20-encrypted MAC address and added functionality to copy it to clipboard.
4. Resolved layout visibility issues by adding background color and spacing adjustments inside frames.

Daily Report: July 07,2025
----------------------------------------------------------------------------------------------------------------------
1. Successfully retrieved the mac address from the login page UI.
2. Added a QComboBox, LicenseKeyGenerator class to incorporate the software selection from the dropdown into the license key generation process
3. Troubleshooting the error "self.software_dropdown = QComboBox()
NameError: name 'QComboBox' is not defined".
4. Modifying the generate_license_key_logic method for generating the license key for the MindME software.

Daily Report: July 08,2025
----------------------------------------------------------------------------------------------------------------------
1. Implementing the licensing in the mindme login UI by encrypting the license key.
2. Attended the demo of Axxonet EEG device

Daily Report: July 09,2025
----------------------------------------------------------------------------------------------------------------------
1. Implemented the function to develop the license key for MindMe software using the MAC address.
2. Modified the serial number generation function by including the motherboard number for the generation of serial number.
3. Troubleshooting the error "Installing collected packages: wmi
ERROR: Could not install packages due to an OSError: [Errno 13] Permission denied: 'C:\\Program Files\\Python38\\readme.rst'"
4. Resolving the issue "from PyQt5.QtGui import QFont, QMovie, Qt
ImportError: cannot import name 'Qt' from 'PyQt5.QtGui'."
5. Implemented the function for the successful generation of the license key for MindeMe software using the MAC address and motherboard serial number.

Daily Report: July 10,2025
----------------------------------------------------------------------------------------------------------------------
1. Explored the required libraries and frameworks for developing the Android/iOS mobile application that integrates with bio-signal-based craving detection system.
2. Implementing of the license validation function for validating the license key.
3. Implementing a function for the analyses of the pattern of license key of smriti and MindMe software
4. Troubleshooting the error encountered while fetching the motherboard serial number from the device.
5. Successfully integrated the spinner function in the updated version of MIME software.

Daily Report: July 11,2025
----------------------------------------------------------------------------------------------------------------------
1. Integrated the EEG analysis UI functions in the live prediction software.
2. Modified the position and size of the scalp map and the cbar in the EEG analysis.
3. Executing and debugging LicenseUI to diagnose license validation failures "Validation failed: Decrypted format incorrect for MindMe. Expected XXXX-XXXXYYYYMMDD".
4. Troubleshooting the error "self.key = b64decode(os.getenv("CHACHA20_KEY", "AAECAwQFBgcICQoLDA0ODxAREhMUFRYXGBkaGxwdHh8="))
NameError: name 'b64decode' is not defined".

Daily Report: July 14,2025
----------------------------------------------------------------------------------------------------------------------
1. Debugging the license UI function to discover the error encountered while the license activation.
2. Resolving the issue in storing the license key for MindMe and Smriti in the registry.
3. Troubleshooting the error "Validation failed: Motherboard suffix mismatch. Expected C2GQ, got 0000".
4. Redesigned the LoginPage UI of MindMe software using CustomTkinter.
5. Resolved the AttributeError: 'PhotoImage' object has no attribute '_PhotoImage__photo' while placing the copy icon in the UI.

Daily Report: July 15,2025
----------------------------------------------------------------------------------------------------------------------
1. Styled the login form on the right with support for dark/light themes with a toolbar, with a theme toggle switch .
2. Added "About" icon that opens a custom CTkToplevel modal displaying the encrypted serial in a styled, read-only input field with tooltip-enabled copy support.
3. Created a tooltip class and added tooltips to icons for identifying each icon.
4. Exploring the functions of random forest in the MiME software.

Daily Report: July 16,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using the random forest classifier and inner speech dataset includes volunteer 3.
2. Hyperparameter exploration was performed on the RandomForestClassifier module from the sklearn.ensemble library, including analysis of default parameter settings.
3. Explored the impact of tree pruning and feature binning techniques on the performance of the Random Forest classifier.

Daily Report: July 17,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is ongoing with the time domain, frequency domain and wavelet domain features using random forest classifier.
2. Encountered an error while training is performed "File "E:\svn repository\MiME_V2pt0\eBrainVoice\module\src\feature\Feature_Pipeline.py", line 336, in feature_extractor
td_fd_feature = channel_feature.reshape(channels_selected, no_of_frames * featCount)
^^{^^}^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: cannot reshape array of size 378 into shape (21,16)".
3. Implemented the code for calculating the time of feature extraction.
4. Explored the different features for improving the accuracy of random forest model.

Daily Report: July 18,2025
----------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the error encountered while training is performed "ValueError: cannot reshape array of size 378 into shape (21,16)".
2. Training is performing using the wavelet features with the inner speech dataset includes the data of volunteer 3.
3. Trained a random classifier model with training accuracy 86.11% and test accuracy 93.34%.
4. Explored the different types of filter that will improve the accuracy of the model.

Daily Report: July 19,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using preprocessing filters: notch filter, bandpass filter, and baseline correction (polynomial degree 2).
2. Hyperparameter tuning of the Random Forest classifier is performed for max_depth, random_state, and n_estimators.
3. Testing is conducted using the trained Random Forest classifier.
4. The evaluated results are documented in an Excel sheet.

Daily Report: July 21,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using the random forest model with time domain and wavelet domain features.
2. Attended the Django and e-health meeting.
3. Testing is conducted using the random forest model with 20% test dataset and individual events.

Daily Report: July 22,2025
----------------------------------------------------------------------------------------------------------------------
1. Data recoding of inner speech is carried out with the Axxonet device.
2. Understanding the concepts of Epidemiology project and explored the dashboard and architecture.
3. Training is performed using the random forest classifier.
4. Prepared the ToT word document of inner speech.

Daily Report: July 23,2025
----------------------------------------------------------------------------------------------------------------------
1. Implementing the logic to open the MindMe software when the user signin using the login page.
2. Troubleshooting the error "from EbrainVoicePipeline import MainUI ModuleNotFoundError: No module named 'EbrainVoicePipeline'n to read the edf file for EEG analysis.
3. Resolving the issue "tkinter.TclError: couldn't open "E:\svn repository\MiME_V2pt0\MiMe_logo.png": no such file or directory"".
FileNotFoundError: events.json not found at path: events.json
can't invoke "event" command: application has been destroyed
4. Created the word document of the accuracy report.

Daily Report: July 24,2025
----------------------------------------------------------------------------------------------------------------------
1. Implementing the logic for the license key activation when the user tries to login using login page.
2. Troubleshooting the error " self.license_ui = LicenseUI(parent=self)
^^{^^}^^^^^^^^^^^^^^^^^
TypeError: LicenseUI.__init__() got an unexpected keyword argument 'parent'"
3. Resolving the issue "class LicenseValidator(threading.Thread):
^^{^^}^^^^
NameError: name 'threading' is not defined"

Daily Report: July 25,2025
----------------------------------------------------------------------------------------------------------------------
1. Modified login page and license UI to open simultaneously when the license is not activated on first run.
2. Reviewed and adjusted LoginPage UI for improved text and frame alignment.
3. Integrated and tested theme toggle button and "About" icon with serial number display.
4. Debugged and tested PyQt5-based UI for license validation and button state functionality.
5. Resolving the error "File "E:\svn repository\MiME_V2pt0\LoginPageUi.py", line 274
self.user_entry.setFont(QFont("Inter", font-size=14)) SyntaxError: expression cannot contain assignment, perhaps you meant "=="?".

Daily Report: July 26,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolved integration conflicts between PyQt and Tkinter frameworks for the license and login UI.
2. Redesigned the login UI by migrating from Tkinter to PyQt to eliminate integration issues.
3. Corrected the disappearance of the theme toggle button and "About" icon by debugging UI.
4. Implemented a popup dialog triggered by the "About" icon click, enabling users to copy the serial number directly.
5. Successfully implemented the licensing with login in the MiME software.

Daily Report: July 28,2025
----------------------------------------------------------------------------------------------------------------------
1. Modify the UI of the license key generator by adding the SenSiz and anesthesia.
2. Added a radio button for choosing the "System and Software".
3. Modified the database for viewing the history of the license key.
4. Successfully the implemented the license key generator tool.
5. Added a json file for adding the software.

Daily Report: July 29,2025
----------------------------------------------------------------------------------------------------------------------
1. Exploring the concepts of the reactjs for building user interfaces for web applications.
2. Understanding the file in the project folder when creating a react application.
3. Understand the concept of hot reload.
4. Explored the components and how to create a component in the reactjs.
5. Developed the demo project for implementing the explored concepts of the reactjs.

Daily Report: July 30,2025
----------------------------------------------------------------------------------------------------------------------
1. Familiarizing the basic concept of javascript.
2. Understanding the document object manipulation(DOM).
3. Training the EEG Former model using inner speech dataset.

Daily Report: July 31,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using the EEG Former classifier using the inner speech dataset(80%) and got the accuracy 33.08%.
2. Testing is performed using EEG Former model with the trained dataset(80%), an accuracy of 33.26%.
3. Testing is performing using EEG Former model with the testing dataset(20%).
4. Exploring the JavaScript concepts - variables, string operations, methods, function reusability.

Daily Report: August 01,2025
----------------------------------------------------------------------------------------------------------------------
1. Testing is performing using EEG Former model with the testing dataset(20%), an accuracy of 29.37%.
2. Training is performed using the SVM classifier with wavelet features and got an accuracy for training - 56.99%.
3. Tested the SVM classifier using the trained dataset with the accuracy - 39.91%.
4. Explored the random and parseInt methods, manipulation of objects, nested objects, recursion in the JavaScript.
5. Completed the basics of the JavaScript.
6. Understood how to create a project and start a development server in the visual studio code.

Daily Report: August 04,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using the KNN classifier using the inner speech dataset an accuracy of 90.92%.
2. Tested the KNN model using the trained dataset got an accuracy of 100%.
3. Exploring the OpenStreetMap.
4. Tested the KNN model using the testing dataset(20%), got an accuracy of 48.95%.
5. Replaced hardcoded absolute paths with dynamic relative paths using os.path.join() and file.

Daily Report: August 05,2025
----------------------------------------------------------------------------------------------------------------------
1. Tested the SVM classifier using the testing dataset(20%) with the accuracy - 29.86%.
2. Encountered the error while testing the SVM model with testing data "File "E:\svn repository\MiME_V2pt0\eBrainVoice\module\execute\executePipeline.py", line 494, in execute_pplfunc
feature_extract_dataframe = extract_feat_pipeline(subject_file_name,
TypeError: feature_extractor() missing 1 required positional argument: 'chunk_data'".
3. Exploring how to integrate OpenStreetMap with the React + Django web application.

Daily Report: August 06,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using the catboost classifier using the innerspeech dataset, an accuracy of 96.82%.
2. Testing the catboost model using the trained dataset(80%), got an accuracy of 100%.
3. Testing the catboost model using the trained dataset(80%), got an accuracy of 72.72%.
4. Exploring how to add map to a reactjs application using leaflet.js.

Daily Report: August 07,2025
----------------------------------------------------------------------------------------------------------------------
1. Attended the meeting for the demo of MiME application.
2. Testing the catboost model using the individual event dataset, got an accuracy of 2 - 83.33%, 8 - 65.3%, 10 - 71.15%.
3. Testing the SVM model using the testing dataset(20%), got an accuracy of 28.67%.
4. Exploring the new Django project creation in the PyCharm.
5. Exploring the channels associated with Broca's and Wernicke's area.
6. Training the random forest model using the 8 channels from Broca's and Wernicke's area using inner speech dataset.

Daily Report: August 08,2025
----------------------------------------------------------------------------------------------------------------------
1. Trained the random forest model using the 8 channels got training accuracy of 92.5%.
2. Tested the random forest model using the trained dataset(80%), got an accuracy of 96.88%.
3. Tested the random forest model using the testing dataset(20%), got an accuracy of 62.93%.
4. Tested the random forest model using the entirely new dataset(V4), got an accuracy of 30%.
5. Implementing a Kerala map in Reactjs using OpenStreetMap.

Daily Report: August 09,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using the random forest classifier and got an accuracy of 89.01%.
2. Tested the trained dataset(80%) with the developed random forest model and got the accuracy of 97.08%.
3. Tested the testing dataset(20%) with the developed random forest model and got the accuracy of 59.44%.
4. Tested the entirely new dataset(V4) with the developed random forest model and got the accuracy of 48.33%.
5. Troubleshooting the error encountered while training the random forest model with 14 channels "File "<__array_function__ internals>", line 200, in result_type, ValueError: at least one
array or dtype is required".
6. Explored the channels that can be used for training the models for inner speech and the identified channels are - C3,C4,F3,F4,F8,P3,P4,T5,T6,T3,T4,FP1 and FP2.
7. Exploring how to implement the OpenStreetMap using react in focusing on the districts in map.

Daily Report: August 11,2025
----------------------------------------------------------------------------------------------------------------------
1. Implementing the OpenStreetMap in the dashboard using Reactjs.
2. Troubleshooting the error "Module not found: Error: Cannot find file: 'kerala_districts.geojson' does not match the corresponding name on disk: '.\src\data\Kerala_districts.geojson'".
3. Resolving the error "src\App.js Line 22:24: 'keralaDistricts' is not defined no-undef".
4. Exploring the geojson file for importing it in the web application.
5. Troubleshooting the "Error: Invalid or missing GeoJSON data. Please check the file".

Daily Report: August 12,2025
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1. Integrating Kerala district boundaries (kerala_districts.geojson) with OpenStreetMap in Reactjs.
2. Resolving the issue "Uncaught (in promise) Error: A listener indicated an asynchronous response by returning true, but the message channel closed before a response was received".
3. Troubleshooting the map rendering issues when parsing GeoJSON polygons.
4. Resolving the error, react-leaflet library to fail when rendering the <GeoJSON> component.

Daily Report: August 13,2025
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1. Troubleshooting the "Error: "Could not establish connection. Receiving end does not exist"".
2. Corrected the file path and placed the file inside the public directory for React to access.
3. Resolving the issue in map layers not displaying in the web application.
4. Exploring other GeoJSON files for each administrative level for building each boundaries.

Daily Report: August 14,2025
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1. Resolving the issue "Uncaught (in promise) SyntaxError: Unexpected token '<', "<!doctype "... is not valid JSON".
2. Investigated why the GeoJSON file was downloading as .geojson.txt instead of .geojson in your ReactJS map project.
3. Looked into Panchayat and Corporation boundary shapefile requirements for Kerala mapping.
4. Exploring possibility of creating maps using ward-level shapefiles with district overlays.

Daily Report: August 18,2025
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1. Exploring the ways to use head urls.txt in PowerShell and debugged why the command wasn’t recognized.
2. Resolving on handling and saving your Kerala GeoJSON files correctly.
3. Explored using shapefiles for panchayat, corporation, and ward boundaries in Kerala maps.
4. Debugging ReactJS Kerala map integration errors (GeoJSON loading and console errors).

Daily Report: August 19,2025
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1. Implemented a PowerShell script to execute the functionality of fetch.sh.
2. Investigated methods for rendering interactive maps from shapefile datasets.
3. Debugged and resolved the PowerShell error: CategoryInfo: ObjectNotFound: (bash:String) [], CommandNotFoundException with FullyQualifiedErrorId: CommandNotFoundException.
4. Automated the download of ward-level GeoJSON files through PowerShell scripting.

Daily Report: August 20,2025
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1. Exploring the map layers of Kerala for developing the map in React.
2. Resolving the frontend errors and connection issues when displaying the map of Kerala.
3. Exploring and fixed issues with loading Kerala GeoJSON data using the ward data.

Daily Report: August 21,2025
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1. Implementing a clear ward boundaries when drill down using style function to the <GeoJSON> layer.
2. Modified the <GeoJSON> to achieve different colors for each level to see boundaries for every zoom.
3. Resolving the error "ERROR in [eslint] src\App.js Line 119:11: 'geojsonLayer' is not defined no-undef".
4. Attended the HTG connect's session.

Daily Report: August 22,2025
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1. Explored to get the Panchayat GeoJSON for plotting in the map.
2. Explored the kerala_lsg_data.geojson that contains Grama Panchayat boundaries.
3. Resolving the issue "ERROR in [eslint] src\App.js
Line 85:9: 'setSelectedPanchayat' is not defined no-undef
Line 96:22: 'wardData' is not defined no-undef
Line 98:11: 'wardData' is not defined no-undef"

Daily Report: August 23,2025
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1. Setting up dynamic district rendering on the map – now districts highlight when clicked.
2. Optimized map zoom levels for district and panchayat boundaries.
3. Testing the panchayat-level boundaries to ensure accuracy of coordinates.
4. Resolving the error where the wards were not loading properly from the JSON files.

Daily Report: August 25,2025
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1. Resolving the issue in viewing the wards when clicking a Panchayat.
2. Troubleshooting the error "404 Not Found" encountered in the web application.
3. Exploring the ways resolve the issue in loading the wards JSON files.

Daily Report: August 26,2025
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1. Reviewing the folder structure (/public/data vs /public/wards) and confirm the exact location of files.
2. Resolving the "Uncaught (in promise) Error: Could not establish connection. Receiving end does not exist."
3. Troubleshooting the issue in loading the wards json files in the filename format downloaded from the git.
4. Resolving the "Uncaught (in promise) SyntaxError: Unexpected token '<', "<!DOCTYPE "... is not valid JSON".

Daily Report: August 27,2025
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1. Resolving the issue in loading the geojson files of the ward data.
2. Preparing the documents for the INNOVISTA.

Daily Report: August 28,2025
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1. CDAC Technopark visit with the part of INNOVISTA.

Daily Report: September 01,2025
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1. Reviewing the code for wards GeoJSON loading issue.
2. Troubleshooting the district → panchayat → ward drill-down issue in React Leaflet map.
3. Exploring the solutions for loading multiple ward GeoJSON files dynamically into the map.

Daily Report: September 08,2025
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1. Verifying the syntax of JSON files to ensure proper structure.
2. Identifying and correcting invalid keys, and other issues in JSON files.
3. Troubleshooting the JSON loading issues in the web application.

Daily Report: September 09,2025
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1. Integrated interactive tooltips and click-to-zoom functionality for each level of the map.
2. Troubleshooting an issue where panchayat GeoJSON was not loading because the DISTRICT property in the file did not match the selected district name.
3. Resolving a problem where features array was empty after filtering, because some panchayats were missing DISTRICT property in the dataset.

Daily Report: September 15,2025
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1. Resolving the ward level loading issue in the web application.
2. Troubleshooting the issue "ERROR in [eslint] src\App.jsLine 1: Parsing error: Unexpected token (1:0)".
3. Resolving the issue "ERROR in ./src/App.jsModule build failed (from ./node_modules/babel-loader/lib/index.js):SyntaxError: E:\kerala-map-taluk\src\App.js: Unexpected token (1:0)".
4. Merged the 1034 ward files into one single file named "kerala_wards_merged.geojson" for resolving the fetching issue.

Daily Report: September 16,2025
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1. Implementing a python script to create district-wise Panchayat GeoJSON files by merging the 1034 ward files.
2. Resolving the error "ERROR in ./src/index.js 17:0-34 Module not found: Error: Can't resolve 'leaflet/dist/leaflet.css' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src'".
3. Troubleshooting the error "npm error enoent Could not read package.json: Error: ENOENT: no such file or directory, open 'E:\Epiform\light-blue-react-master\package.json'".
4. Integrating the code for wards wise drilldown in the dashboard.

Daily Report: September 17,2025
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1. Resolving the error "Cannot read properties of undefined (reading 'Panchayat') TypeError: Cannot read properties of undefined (reading 'Panchayat')".
2. Troubleshooting the issue while reading the json "Cannot read properties of undefined (reading 'name') TypeError: Cannot read properties of undefined (reading 'name')".
3. Resolving the "ERROR in ./src/pages/dashboard/components/am4chartMap/am4chartMap.js 10:0-38 Module not found: Error: Can't resolve './data/wards.json' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'".
4. Training the CNN-BiLSTM model using the MiME software.
5. Encountered the error while training the deep learning model File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py", line 169, in _assert_all_finite_element_wise raise ValueError(msg_err) ValueError: Input contains NaN.
5. Resolving the error - ERROR in ./src/pages/dashboard/components/am4chartMap/am4chartMap.js 6:0-39 Module not found: Error: Can't resolve './wards_index' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'

Daily Report: September 18,2025
----------------------------------------------------------------------------------------------------------------------
1. Implementing ward level drill down in the dashboard using react in the Kerala map.
2. Troubleshooting the errors occurred while rendering the map:-
1. Module not found: Error: Can't resolve './wards_index'
2. Uncaught TypeError: Cannot read properties of undefined (reading 'Panchayat')
3. Expected a string or number but got [object Function]
4. Module not found: Error: Can't resolve './districtsGeoJson.json' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'
5. ERROR in ./src/pages/dashboard/components/am4chartMap/am4chartMap.js 9:0-49
6. Module not found: Error: Can't resolve './panchayats_index' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'
3. Training the CNN-LSTM deep learning model with time domain and wavelet domain features using the 80% split of the inner speech dataset.
4. Encountered an error while training the CNN-LSTM model - td_fd_feature = channel_feature.reshape(channels_selected, no_of_frames * total_TD)
ValueError: cannot reshape array of size 210 into shape (21,8).

Daily Report: September 19,2025
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1. Training the CNN_LSTM deep learning models using the inner speech dataset without features.
2. Implementing the ward level drill down in the Kerala map using the json files.
3. Troubleshooting the errors encountered while loading the ward json "am4chartMap.js:187 No taluks found for district: undefined".
4. Troubleshooting the errors "am4chartMap.js:141 Uncaught TypeError: Cannot read properties of undefined (reading 'Panchayath')".
5. Resolving "Module not found: Error: Can't resolve '../diseaseReport/wards' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'".

Daily Report: September 20,2025
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1. Encountered an error while training the CNN_LSTM model " File "<__array_function__ internals>", line 200, in result_type ValueError: at least one array or dtype is required".
2. Resolving the "ERROR Cannot read properties of undefined (reading 'Panchayath') TypeError: Cannot read properties of undefined (reading 'Panchayath')".
3. Implementing the functionality to view the wards when click on a panchayat in the dashboard.
4. Troubleshooting the failure, when clicking a panchayat polygon taking the ward from the json.

Daily Report: September 22,2025
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1. Training is carried out using the CNN-LSTM deep learning model using the wavelet features.
2. Implementing a python script to merge the 1034 ward files to a single json for loding the map to the dashboard.

Daily Report: September 23,2025
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1. Training is carried out using the ANN deep learning model using the wavelet features and filters.
2. Resolving the issue "Failed to resolve import "@amcharts/amcharts4/core" from "src/components/MapView.jsx"".
3. Troubleshooting the error when merged district wise wards are loaded in the code "Cannot read properties of undefined (reading 'LSGD') TypeError: Cannot read properties of undefined
(reading 'LSGD')".
4. An error is encountered while training the CNN-LSTM model " File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py", line 169, in
_assert_all_finite_element_wise raise ValueError(msg_err) ValueError: Input contains NaN."

Daily Report: September 24,2025
----------------------------------------------------------------------------------------------------------------------
1. Training is performed using the ANN model using the wavelet features with innerspeech dataset.
2. Encountered an error while traini9ng the ANN model "File "C:\Users\HTG\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\utils\validation.py", line 169, in _assert_all_finite_element_wise raise ValueError(msg_err) ValueError: Input contains NaN."
3. To resolve the error i the model training enabled the channel selection, but error is encountered " raise ValueError(msg_err) ValueError: Input contains NaN."
4. Resolving the error "Cannot read properties of undefined (reading 'LSGD') TypeError: Cannot read properties of undefined (reading 'LSGD')"
5. Explored the ward level JSON of all the districts in one file.
6. Implementing the ward level drill down using the "KL_Wards.geojson" file in the dashboard.

Daily Report: September 25,2025
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1. Implementing the Kerala map with ward level drill down using the "KL_Wards.geojson".
2. Troubleshooting the error "exmgraph.js:102 Error fetching data: TypeError: Failed to fetch at exmgraph.js:89:1 at commitHookEffectListMount (react-dom.development.js:20059:1)".
3. Resolving the error while fetching the KL_Wards.geojson file.

Daily Report: September 26,2025
----------------------------------------------------------------------------------------------------------------------
1. Exploring the structure and properties of the geojson "KL_Wards.geojson".
2. Implementing the ward level drill down in the Kerala map using the "KL_Wards.geojson".
3. Troubleshooting the error "Module not found: Error: Can't resolve 'turf/turf' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'".
4. Familiarizing the code that displaying the map and its drill down functionality that fetch different json files.
5. Resolving the "npm error Could not resolve dependency: npm error peer react"^16.4.0 || ^17.0.0" from connected-react-router@6.9.3".

Daily Report: September 27,2025
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1. Implementation of the ward level map using the "KL_Wards.geojson".
2. Resolving the json fetching issue in the ward level drill down "am4chartMap.js:274 No wards found for panchayat: Kumily Gramapanchayath showWards @ am4chartMap.js:274".
3. Exploring the "KL_Wards.geojson" for resolving the fetching issue of the ward names.

Daily Report: September 29,2025
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1. Debugging the code of the ward level drill down to trace the flow of data.
2. Resolving the issue "Error fetching data: TypeError: Failed to fetch at exmgraph.js:45:1".
3. Implemented the labelling of the panchayat in the dashboard from Panchayat.json file.

Daily Report: September 30,2025
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1. Resolving the errors encountered while implementing the ward level drill down in the dashboard.
1. Uncaught (in promise) Error: Could not establish connection. Receiving end does not exist.
2. 3000/#/app/main/dashboard:1 Uncaught (in promise) Error: A listener indicated an asynchronous response by returning true, but the message channel closed before a response was received
3. Error: am4geodata_wards or features is undefined or not an array
4. exmgraph.js:28 Error fetching data: TypeError: Failed to fetch at exmgraph.js:24:1

Daily Report: October 03,2025
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1. Resolving the error " am4chartMap.js:207 Error loading ward file: SyntaxError: Unexpected token '<', "<!DOCTYPE "... is not valid JSON".
2. Reframe the folder structure of the ward data for fetching the coordinates.
3. Check your wards file exists by starting the dev server and try opening the json in the browser.
4. Resolving the issue that the ward files is not being served by the dev server.

Daily Report: October 06,2025
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1. Comparing the Panchayat.json file and ward json files to know the structure of the files.
2. Exploring the Coordinate Reference System using in the Panchayat.json file.
3. Analyzing why the ward files failed to load in the dashboard.

Daily Report: October 07,2025
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1. Analyzing and validating the ward json files for ensuring the structure.
2. Created a single-file HTML viewer in the canvas of viewing the json files individually.
3. Resolving the issue "SyntaxError: Unexpected non-whitespace character after JSON at position 9031 (line 2 column 1)".
4. Refactored the HTML-based viewer into a React application that displays JSON files using a dropdown selector.

Daily Report: October 08,2025
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1. Resolving map is showing small tiles all over the page instead of one full Kerala map.
2. Troubleshooting the error "Map container is already initialized. at NewClass._initContainer (http://localhost:3001/static/js/bundle.js:6429:15) at NewClass.initialize".
3. Resolving the error "E:\geojson-viewer\src\wardsList.js Failed to compile. Module not found: Error: Can't resolve './wardsList' in 'E:\geojson-viewer\src\components'".ist' in 'E:\geojson-viewer\src\components'.
4.Resolving the error when loading the panchayat json main.5cd8c0e2d5b086c…a.hot-update.js:157 Failed to load panchayaths: Error: Bounds are not valid.

Daily Report: October 10,2025
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1. Resolving the ERROR in ./src/pages/dashboard/components/am4chartMap/am4chartMap.js 8:0-43 Module not found: Error: Can't resolve '../../../wardsList' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'.
2. Troubleshooting the issue "this.wardLayerRef.current.getBounds is not a function TypeError: this.wardLayerRef.current.getBounds is not a function at Am4chartMap.loadWards (http://localhost:3000/main.f337ade09c31027aa856.hot-update.js:191:48)".
3. Implementing the ward loading with suffix GramaPanchayat and encountered error "Uncaught Error: Map container is already initialized. at NewClass._initContainer (Map.js:1094:1)".
4. Exploring the how the names are stored in ward GeoJSON files vs the panchayath.json file.

Daily Report: October 13,2025
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1. Modifying the design of the Kerala map, stretch to full width & height.
2. Implementing a option to view in the map in full screen.
3. Resolving the "ERROR in ./src/pages/dashboard/components/am4chartMap/am4chartMap.js 17:0-34 Module not found: Error: Can't resolve 'leaflet-control-geocoder' in
'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'".
4. Troubleshooting the issue in installing a Leaflet plugins for adding styling features.

Daily Report: October 14,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving the issue "leaflet__WEBPACK_IMPORTED_MODULE_1___default(...).Control.Fullscreen is not a constructor
TypeError: leaflet__WEBPACK_IMPORTED_MODULE_1___default(...).Control.Fullscreen is not a constructor".
2. Remodify the bulky borders of the dropdown with minimal size.
3. Changed the map with minimal color palette of viewing more professional.
4. Added a back button functionality for drilling down to Ward ➝ Panchayat ➝ District.

Daily Report: October 15,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving the bug in fetching some Panchayat names in panchayath.json have extra suffixes like “Gramapanchayat”.
2. Exploring the ward files for verifying the wards of each panchayat.
3. Resolving the dropdown option for viewing the wards list not working issue.

Daily Report: October 16,2025
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1. Redesigning the map to only view the Kerala map, not the entire world map.
2. Designing the map with suitable colors for improving the visibility.
3. Identifying the issue in the Thiruvananthapuram panchayat data not loading in the map.

Daily Report: October 17,2025
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1. Resolving the spelling mismatches (like Thiruvananthapuram vs Thiruvanathapuram) that breaks the dropdown from listing panchayats.
2. Troubleshooting the issue "Module not found: Error: Can't resolve './district.json' in 'E:\DASHBOARD_EPIFORM\light-blue-react-master\src\pages\dashboard\components\am4chartMap'".
3. Identifying why the dropdown was failing to list some panchayats from different districts and not getting their wards.

Daily Report: October 21,2025
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1. Analyses through the coding part for identifying the naming issues in json file.
2. Implementing a function that removes "grama panchayat" and "gramapanchayat" suffixes from the ward data.
3. Fixing the issue in viewing the panchayat name with the suffix gramapanchayat in the dashboard.
4. Analyzing the lag lag when a panchayat is clicked in the dashboard.

Daily Report: October 22,2025
----------------------------------------------------------------------------------------------------------------------
1. Analyzing the each panchayat of each district for verifying the spelling mismatch and ward loading issues.
2. Currently analyzed 5 districts- Thiruvananthapuram, Kollam, Pathanamthitta, Alappuzha, Kottayam.

Daily Report: October 23,2025
----------------------------------------------------------------------------------------------------------------------
1. Analyzing the each panchayat of each district and covered 5 districts- Kottayam, Idukki, Ernakulam, Thrissur, Palakkad.

Daily Report: October 24,2025
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1. Implementing the panchayat names to appear as visible map labels when click a district instead of showing only on hover tooltips.
2. Troubleshooting the error "ERROR [eslint] src\pages\dashboard\components\am4chartMap\am4chartMap.js Line 221:10: 'found' is not defined no-undef".
3. Analyzing the each panchayat of each district and covered 5 districts- Malappuram, Kozhikode, Wayanad, Kannur, Kasargod.

Daily Report: October 25,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving the issues in the ward level drill down in the dashboard.
2. Verifying the spelling of Panchayat's name in ward json files and panchayat.json file.

Daily Report: October 27,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving the drill-down issue in certain panchayats by verifying name consistency between the ward and panchayat JSON files.
2. Successfully fixed the issue for three panchayats — Alappuzha, Kottayam, and Idukki.

Daily Report: October 28,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving the drill-down issue in certain panchayats and successfully fixed the issue for three panchayats — Ernakulam, Thrissur, Palakkad.

Daily Report: October 29,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolved the name inconsistency between the ward and panchayat JSON files.
2. Successfully fixed the issues for all panchayats.

Daily Report: October 30,2025
----------------------------------------------------------------------------------------------------------------------
1. Verifying the panchayat borders with the ward boundaries of various districts in the dashboard.
2. Exploring the different panchayat json files for plotting.

Daily Report: October 31,2025
----------------------------------------------------------------------------------------------------------------------
1. Exploring connecting React (frontend) with Django (backend).

Daily Report: November 01,2025
----------------------------------------------------------------------------------------------------------------------
1. Exploring how the Django works and follows the MVT design pattern.
2. Understanding the concept of Object Relational Mapping.

Daily Report: November 04,2025
----------------------------------------------------------------------------------------------------------------------
1. Exploring the Django python framework for connecting Django with reactjs web application.
2. Successfully installed the Django in the project.
3. Encountered any error while running the server "Traceback (most recent call last):File "E:\Django\epiform\manage.py", line 11, in main from django.core.management import execute_from_command_line ModuleNotFoundError: No module named 'django'"

Daily Report: November 06,2025
----------------------------------------------------------------------------------------------------------------------
1. Troubleshooting the error "File "E:\Django\epiform\manage.py", line 13, in main raise ImportError( ImportError: Couldn't import Django)."
2. Resolved the installation error and successfully installed the Django version 5.2.8.
3. Exploring the methods to connect the Django with React.

Daily Report: November 07,2025
----------------------------------------------------------------------------------------------------------------------
1. Understanding the ways to connect React with Django as separate frontend and backend.
2. Troubleshooting the error ""E:\DASHBOARD_EPIFORM\backend\venv\Scripts\pip.exe\__main__.py", line 5, in <module> ModuleNotFoundError: No module named 'pip'".
3. Successfully connected the React (frontend) with Django (backend).

Daily Report: November 10,2025
----------------------------------------------------------------------------------------------------------------------
1. Exploring how to build a web app using Django.
2. Troubleshooting the error "E:\Django_Example01>virtualenv broenv 'virtualenv' is not recognized as an internal or external command, operable program or batch file."
3. Resolving the issue "[10/Nov/2025 12:22:09] "GET / HTTP/1.1" 200 10664 Not Found: /hello/ [10/Nov/2025 12:22:19] "GET /hello/ HTTP/1.1" 404 2101".

Daily Report: November 11,2025
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1. Resolving the error "File "<frozen importlib._bootstrap>", line 973, in _find_and_load_unlocked ModuleNotFoundError: No module named 'hello_django.settings'".
2. Analyzing the folder structure for solving the issue in running the server.
3. Installing the PostgreSQL for managing the database in Django.

12/11/2025
Prepared architecture for bci dataset and algorithm creation proposal
anesthesia model building ui correcting
meditation ui modification error correction

Daily Report: November 13,2025
----------------------------------------------------------------------------------------------------------------------
1. Exploring how a HTTP request is processed by urls.y file.
2. Understanding the concept of views.py file in generating the HTTP repsonse.
3. Exploring the DjangoTemplate Language.
4. Resolving the error "TemplateDoesNotExist at /hello/ hello.html.

Daily Report: November 14,2025
----------------------------------------------------------------------------------------------------------------------
1. Work transfer from Sherin - Setting up the environment for running the Fast API.
2. Resolving the error "PS E:\EPIFORM_FILE_TRANSFER\Fast_API> python -m uvicorn api_test:app --reload >> C:\Program Files\Python38\python.exe: No module named uvicorn".
3. Troubleshooting the issue " File "E:\EPIFORM_FILE_TRANSFER\Fast_API\api_test.py", line 4, in <module> from delta import DeltaTable ImportError: cannot import name 'DeltaTable' from 'delta' (C:\Program Files\Python38\lib\site-packages\delta\__init__.py)".
4. Resolving the uvicorn issue that shows the error "The system cannot find the path specified".

Daily Report: November 17,2025
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1. Analysis of the project for understanding the connection of FastAPI, Django and React.
2. Understanding the components used for developing the dashboard.
2. Preparing the workflow document of how React, Django, and FastAPI work together.

Daily Report: November 18,2025
----------------------------------------------------------------------------------------------------------------------
1. Meeting for the further development of the epiform dashboard which includes the map redesigning, layouts and adding of other components.
2. Exploring the different templates of web page development.

Daily Report: November 19,2025
----------------------------------------------------------------------------------------------------------------------
1. changing the Kerala map theme to the matching theme of the analysis Kerala map.
2. Adding the hovering effect in the map.
3. Fixing the label name of district with auto centroid.

Daily Report: November 20,2025
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1. Implementing the auto hovering in the Kerala map with the project upwards.
2. Troubleshooting the error - Uncaught TypeError: number 76.37333911957222 is not iterable (cannot read property Symbol(Symbol.iterator))
3. Resolving the error - Uncaught Error: Element type is invalid: expected a string (for built-in components) or a class/function (for composite components) but got: object.
4. Implementing the feature to pause the hovering effect when the mouse points in the map.
5. Adding the feature to fade out the auto hovering when the mouse points and resume the auto hovering the points out.

Daily Report: November 21,2025
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1. Implementing a header above the map for displaying the name and placing the login button
2. Implmenting the feature that Manual Hover Should Animate with Lift and Glow.
3. Moving the "Live" badge and last updated time moved OUTSIDE the header.
4. Redesigning the login page with a dark background appears behind the floating label.

Daily Report: November 24,2025
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1. Implementing a disease card for displaying the statistics of the diseases confirmed, suspected and death.
2. Implementing a function to retrieve district-wise statistics from FastAPI through Django backend to the UI.
3. Redesigning the disease card for matching with the dashboard theme.
4. Resolved the errors encountered while fetching the data from the API.

Daily Report: November 25,2025
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1. Designing the UI to be responsive irrespective of any screen resolution.
2. Redesigning the disease panel with a table view structure and placed to the right center in the dashboard.
3. Exploring the implementation of the functionality to view the polygon of each district with auto hovering.

Daily Report: November 26,2025
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1. Implementing the feature when auto-hover points to a district a separate copy of that district’s shape should appear on the left side of the dashboard.
2. Resolving the error "src\pages\maps.js Line 269:25: 'chart' is not defined no-undef Line 376:30: 'p' is not defined no-undef webpack compiled with 1 error".
3. Troubleshooting the error "this._flyingLayer.addChild is not a function TypeError: this._flyingLayer.addChild is not a function".

Daily Report: November 27,2025
----------------------------------------------------------------------------------------------------------------------
1. Resolving "src\pages\maps.js Line 524:24: 'polygon' is not defined no-undef".
2. Troubleshooting the error "Uncaught runtime errors: ERROR this._flyingLayer.addChild is not a function TypeError: this._flyingLayer.addChild is not a function".
3. Resolving the error encountered when the implementation of the the feature when auto-hover points to a district a separate copy of that district’s shape should appear
"Cannot read properties of undefined (reading 'getPoint') TypeError: Cannot read properties of undefined (reading 'getPoint')".
4. Troubleshooting the error "Could not attach clone to flyingLayer safeAddToFlyingLayer @ maps.js:393 Series.ts:1435 Error: EventDispatcher is disposed".

Daily Report: November 28,2025
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1. Implementing the district preview in a separate frame on the left side of the UI.
2. Resolving "ERROR Cannot access 'previewSeries' before initialization ReferenceError: Cannot access 'previewSeries' before initialization".
3. Troubleshooting the error "ERROR Cannot read properties of undefined (reading 'dataItem') TypeError: Cannot read properties of undefined (reading 'dataItem')".
4. Resolving the Uncaught TypeError: Cannot read properties of undefined (reading 'series').

Daily Report: December 01,2025
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1. Troubleshooting the error "ERROR in [eslint] src\pages\LoginPage.js Line 1: Parsing error: This experimental syntax requires enabling one of the following parser plugin(s): "decorators", "decorators-legacy". (1:0)".
2. Resolving the issue "ERROR in ./src/pages/LoginPage.js Module build failed (from ./node_modules/babel-loader/lib/index.js)".
3. Implementing the header in the login page with new login design.

Daily Report: December 02,2025
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1. Modifying the placement of the data chart to the bottom and increase the height of the panchayat map frame in the dashboard.
2. Converted the disease radio buttons to the dropdown inside the panchayat map.
3. Resolving the issue of dropdown background and invisible text in the dropdown.
4. Implementing a date selector in the data chart and updating bar colors to match dashboard theme.

Daily Report: December 03,2025
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1. Redesigning the colored left-side bar for changing the color for each disease card.
2. Resolving the issue "Cannot read properties of undefined (reading 'dataItem') TypeError: Cannot read properties of undefined (reading 'dataItem')".
3. Implementing the GPS locator icon on the Preview Map, only if confirmed > 0.
4. Troubleshooting the error "gpsCircle.createChild is not a function TypeError: gpsCircle.createChild is not a function".

Daily Report: December 04,2025
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1. Implementing the GPS locator in the preview map in the UI.
2. Troubleshooting the "ERROR Cannot read properties of null (reading 'Thiruvananthapuram') TypeError: Cannot read properties of null (reading 'Thiruvananthapuram')".
3. Resolving the "Uncaught TypeError: Cannot read properties of undefined (reading 'addGroup')".

Daily Report: December 05,2025
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1. Implementing a pin shaped GPS locator inside the preview map for marking the confirmed disease cases.
2. Add the count inside the GPS icon for understanding how much cases are confirmed for each disease.
3. Resolving the "ERROR in ./src/pages/maps.js 671:22-42 export 'ease'.'sineOut' (imported as 'am4core') was not found in '@amcharts/amcharts4/core'.
4. Troubleshooting the error encountered while implementing the pin icon "ERROR pin.createChild is not a function TypeError: pin.createChild is not a function".

Daily Report: December 08,2025
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1. Implementing the function to hide preview map initially until first district is hovered.
2. Implemented the function to reduce to the timer of loading the count of the diseases.
3. Fixing the error that the mini map doesn't showing the count when initially loaded.

Daily Report: December 09,2025
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1. Implementing the feature to zoom in and out the mini district map using the mouse wheel and double click.
2. Fixing the GPS locators placement at the true geometric center of each district in the mini map.

Daily Report: December 10,2025
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1. Implementing the Navigation Tabs: Surveillance, Disease Modelling, Forecasting.
2. Redesigning the navigation tabs to the modern layout.
3. Redesigning the header of the dashboard to clear view in large screens.

Daily Report: December 11,2025
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1. Integrating the navigation bar with the header on the right side by creating a .header-right container.
2. Exploring the different styles of navigation bar and its alignment in the UI.
3. Designed a logo for replacing the current logo in the UI.
4. Fixing the issue of displaying the logo in the UI.
5. Designing the navigation tabs with smooth animations, hover effects, and styling that matches the EPIFORM dashboard theme.

Daily Report: December 12,2025
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1. Changed the header of the login page to the same as dashboard header in the home page.
2. Adding the header from maps.js to the Dashboard.js page with a reduced size.
3. Troubleshooting the ERROR in ./src/components/PanchayatMap.js Module build failed (from ./node_modules/babel-loader/lib/index.js): SyntaxError: E:\EPIFORM_FILE_TRANSFER\theme-dashboard\src\components\PanchayatMap.js: Unexpected token, expected "," .

Daily Report: December 16,2025
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1. Integrated the header into the surveillance dashboard.
2. Adjusted the layout of the data chart and panchayat map to enable a complete view without scrolling.
3. Redesigned the data chart colors and bar chart styling to enhance the overall appearance of the dashboard.

Daily Report: December 19,2025
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1. Fixing the alignment to zoom independence for displaying in any screen.
2. Resolving the error "map.invalidateSize is not a function TypeError: map.invalidateSize is not a function".
3. Troubleshooting the issue occurred while adjusting the district panel "Error: <feOffset> attribute dx: Expected number, "Infinity".

Daily Report: December 20,2025
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1. Redesigning the surveillance dashboard with changing the icon and font of the navigation tabs.
2. Changing the layout of the preview map and disease count panel to the right side.
3. Rearranging the map to the left side of the UI.

Daily Report: December 21,2025
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1. Removing the logo and changing the padding to zero for aligning the header title.
2. Installing and exploring lucid react for getting icons.
3. Designing the panchayat map to avoid conflict with Kerala map design.
4. Resolving the error "Uncaught runtime errors:ERROR badge.createChild is not a function TypeError: badge.createChild is not a function".
5. Redesigning the data chart for better visibility.

Daily Report: December 22,2025
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1. Implementing the change in the UI to add "Government of Kerala" to the header.
2. Changed the color of the navigation tabs and the hovering effect.
3. Resized and reduced the opacity of the titles in the header.
4. Troubleshooting the error "Module build failed (from ./node_modules/babel-loader/lib/index.js): SyntaxError: E:\EPIFORM_FILE_TRANSFER\theme-dashboard\src\pages\Dashboard.js: Only one default export allowed per module. (62:0)".
5. Attended the meeting of the EPIFOM demo.

Daily Report: December 24,2025
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1. Creating a descending font size from EPIFOM to Government of Kerala.
2. changed the navigation tab colors to white by default with the current color on hover.
3. Resolving the error "Uncaught runtime errors: × ERROR map.chartContainer.onPrivate is not a function TypeError: map.chartContainer.onPrivate is not a function".

Daily Report: December 29,2025
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1. Exploring the difference between FASTAPI and RESTAPI.
2. Exploring the ways to convert a Django project to Django Rest framework project.
3. Resolving the error "TypeError: 'module' object is not iterable".

Daily Report: December 30,2025
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1. Implementing the conversion from Django to Django Rest framework.
2. Resolving the error "File "E:\EPIFORM_FILE_TRANSFER\django_api\venv\lib\site-packages\django\urls\resolvers.py", line 725, in url_patterns raise ImproperlyConfigured(msg.format(name=self.urlconf_name))".
3. Implementing a launching page using react-vite and tailwind.

Daily Report: December 31,2025
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1. Designing the landing page with gradient background and theme switching icon on the right.
2. Adding the animated floating elements and subtle animations on the background in the UI.
3. Redesigned the UI with neon cyan combinations to match with other UIs.

Daily Report: January 02,2026
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1. Enlarged the title in the header to text-4xl font-extrabold.
2. Added tracking-wide for better letter spacing in the title which is placed in the header.
3. Converting the entire React project from Create React App to Vite + Tailwind CSS for better visibility.
4. Resolving the error "ren : Cannot rename the specified target, because it represents a path or device name.".
5. Troubleshooting the error "file: E:/EPIFORM_FILE_TRANSFER/theme-dashboard/src/index.css:237:4 at Input.error (E:\EPIFORM_FILE_TRANSFER\theme
dashboard\node_modules\postcss\lib\input.js:135:16)".

Daily Report: January 05,2026
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1. Converted the project into react tailwind, from js to jsx files.
2. Exploring the implementation of tailwind in react.
3. Exploring the best folder structure for a web development project.

Daily Report: January 07,2026
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1. Added “Epidemic Intelligence” as a new navigation tab to the right of the existing tabs.
2. Changed the design of the header to gradient theme with change in font size and family.
3. Troubleshooting the error "Uncaught ReferenceError: Activity is not defined at MapsPage.render (maps.jsx:1177:20)".
4. Changed the background of the navigation tabs which match with the header theme.
5. Resolving the error "Uncaught (in promise) Error: A listener indicated an asynchronous response by returning true, but the message channel closed before a response was received".

Daily Report: January 08,2026
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1. Implementing the header components to a reusable file for viewing it in all pages.
2. Fixed the issue with the "Government of Kerala" text not displaying in the surveillance dashboard.
3. Exploring the folder structure for integrating the other projects in the theme-dashboard project.

Daily Report: January 09,2026
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1. Increased the district name size in the preview map from text-xs to text-xl and added font-semibold to make it more readable.
2. Exploring the folder structure for the reactjs -Django projects.
3. Extracted the header and navigation bar from maps.jsx into a reusable Header component (Header.jsx).
4. Troubleshooting the error "index.jsx:1 Failed to load resource: the server responded with a status of 404 (Not Found)".

Daily Report: January 12,2026
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1. Updated the count boxes to have light background colors matching each disease.
2. Transformed the UI into gradient backgrounds, glassmorphic panels, disease cards with bold colors.
3. Added badges and indicators to the disease outbreak monitor in the UI.
4. Unified the backgrounds for maps and panels to same color for for viewing as a single frame.
5. Exploring how to create a React project with a perfect, industry-standard folder structure.

Daily Report: January 14,2026
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1. Implementing a common login on the right side of the navigation and remove the login from navigation tabs, as well as remove the "last updated" text.
2. Implementing the header consistent across all pages by extracting the header from MapsPage.jsx and making it reusable.
3. Added a label near the district name in the preview map to clarify that the GPS icon count shows confirmed cases.
4. Combined the two separate headings ("District View" and "Disease Outbreak Monitor") into a single unified heading called "Disease Outbreak Monitor".

Daily Report: January 15,2026
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1. Changed the login button to a user profile icon when logged in, and clicking it should show a dropdown with "Hello [username]" and a logout option.
2. Resolving the issue that the dropdown is being clipped by its parent container ad it is partially visible in the UI.
3. Troubleshooting the error "[plugin:vite:react-babel] E:\EPIFORM_FILE_TRANSFER\theme-dashboard\src\modules\maps\pages\MapsPage.jsx: Unexpected keyword 'this'. (1384:19)".
4. Implementing the function to display all the navigation tabs in inactive state before login.

Daily Report: January 16,2026
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1. Moved the navigation tabs and login/user dropdown into the Header component so they persist across all pages.
2. Creating an AuthContext for global authentication state management and update the Header component to include navigation and auth.
3. Redesigned the login page to be a modal/popup overlay on the same page, and after logging in, the user stays on the current page.
4. Updated the Header component to redirect to the home page (MapsPage) when the user logs out.

Daily Report: January 19,2026
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1. Removed the pulsing outer circle that implemented in the GPS locator.
2. Exploring how to convert a shape file to geojson file.
3. Installed and converted the shape file to geojson file using QGIS software.

Daily Report: January 20,2026
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1. Integrating the Government hospital point markers in the UI loaded from local GeoJSON in the preview map.
2. Troubleshooting the error "Uncaught SyntaxError: Unexpected token ':'".
3. Removed the icons from disease count box.
4. Identified and corrected the "? Active" title in the disease outbreak monitor.

Daily Report: January 21,2026
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1. Implemented a functionality to traverse back to the Home page using the EPIFOM logo.
2. Added a tooltip to "Back to Home" in the EPIFOM logo for identifying the feature.
3. Changed the GPS locator colors to match exactly the disease count box colors with the same glow gradient effect.
4. Removed the white circle inside GPS locators, showing the count in white font color instead.

Daily Report: January 22,2026
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1. Removed the outer frame and successfully merged the panchayat map and data charts into a single frame to save space.
2. Reduced the width of the Kerala map frame for appending the data chart and panchayat map.
3. Redesigning the disease surveillance UI by changing the theme to purple color.
4. Changed the data chart from bar chart to line chart.

Daily Report: January 23,2026
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1. Implementation of a functionality, new date filtering capabilities that allow users to analyze disease data trends across different time periods.
2. Fixed the authentication persistence issue on page reload.
3. Troubleshooting the error "PanchayatMapNew.jsx:137 Uncaught ReferenceError: Cannot access 'getWeekNumber' before initialization at PanchayatMapNew.jsx:137:25".
4. Successfully fixed the zooming issues in map components by optimizing the zoom controls.
5. Modified the layout to place date filter on the same line as the disease dropdown.