EEG Spectrogram: A Python Flask EEG Analysis Tool Development - Part 3

In this video, I demonstrate a web application designed for EEG (Electroencephalogram) data visualization. The application features a spectrogram that displays seizures on different channels. I showcase how the application defaults to a 10-second display, which can be adjusted, and discuss the benefits of using a logarithmic scale for better detail in lower frequency components. I also delve into the functionality of the wavelet denoising feature and its impact on the EEG signal and spectrogram.

Throughout the video, I explore the code behind the application, focusing on the use of the Plotly library for graph rendering and Flask for backend data fetching. I address specific issues, such as the need to update the EEG spectrogram in response to changes in wavelet denoising settings. By examining the JavaScript code, I demonstrate how user interactions with sliders and toggles can dynamically update EEG signal visualizations in real-time.

Timestamps:
(00:00) Introduction and overview of the EEG data visualization application
(00:31) Discussion on logarithmic vs. linear scale
(01:03) Reviewing and understanding the provided code
(02:01) Detailed explanation of the EEG web application's HTML and JavaScript code
(03:37) Identifying and addressing issues with wavelet denoising and spectrogram updates
(05:17) In-depth analysis of the wavelet denoising function and its impact on the EEG spectrogram
(06:24) Implementing changes to update the spectrogram based on denoising settings

I use GitHub Copilot and ChatGPT for development.