Building EEG Spectrogram Models for Seizure Detection: Code Debugging & Model Insights
Welcome to my latest video, where I take you through the process of debugging and refining an interactive web application designed for EEG spectrogram analysis. This project focuses on seizure detection using convolutional neural networks (CNNs), helping to visualize and understand complex biomedical data.
In this session, I address JavaScript errors, HTML structure issues, and model result displays, all while highlighting key features like accuracy charts, confusion matrices, and misclassified images. I also discuss how changes to model settings like batch size and learning rate impact performance.
This web app is built around the CHB-MIT scalp EEG database, providing a unique platform for exploring seizure and non-seizure EEG data through machine learning models. Whether you're interested in biomedical data science or just curious about how AI models analyze neurological conditions like epilepsy, this video has something for you.
Check out the live project here: https://bionichaos.com/RhythmScan.
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#EEGAnalysis #SeizureDetection #MachineLearning #BiomedicalData #CNNModels #JavaScriptDebugging #EpilepsyResearch
0:00 Introduction and overview of the debugging process
0:07 Initial thoughts on generating code and responses
0:17 Spectrogram visualization and initial model result discussion
0:40 Addressing JavaScript errors and debugging issues
1:01 Resolving HTML structure errors and unexpected results
2:12 Refining model results display with JavaScript fixes
3:04 Testing and debugging model run outputs
4:59 Discussion on handling multiple training runs
5:46 Adjusting default display settings for better usability
7:11 Reviewing changes in JavaScript code and their effects
8:04 Incorporating misclassified images into the results display
10:05 Highlighting the importance of logs and training summaries
11:11 Overview of EEG spectrograms and CHB-MIT database
12:20 Metrics for evaluating model performance and debugging
13:39 Plans for future improvements and experiments
14:52 Final thoughts, questions, and viewer interaction
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