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Mastering EEG Data Visualization: Plotly vs. Chart.js Showdown

Hey everyone, welcome back to my channel! In today's video, we dive deep into EEG data visualization techniques based on data from the Kaggle competition "Harmful Brain Activity Classification." We're exploring the differences between using Chart.js and Plotly.js for plotting spectrograms. I'll take you through the issues we encountered with Chart.js and why we decided to switch to Plotly. We'll load the spectrogram data, troubleshoot JavaScript errors, and ensure accurate data parsing. You'll see how to dynamically fetch and update spectrograms in a Flask application and how GitHub Copilot assists in generating the necessary code. Don't forget to check out BioniChaos.com for more tools and resources! In this technical deep dive, we address the complexities involved in transitioning from Chart.js to Plotly.js for EEG spectrogram visualization. Initially, we encountered challenges with Chart.js due to its limited capabilities in rendering complex spectrogram data effectively. As we progressed, I demonstrated the process of integrating Plotly.js, which offers more robust features for scientific visualizations, such as dynamic scaling and more intuitive interaction with the plotted data. The video also covers troubleshooting techniques, code optimization strategies, and performance considerations to ensure efficient data handling and rendering in a Flask-based web application environment. Throughout the video, I delve into the specifics of handling EEG data formats and optimizing them for visualization with Plotly.js. I discuss the significance of accurate data parsing and the impact of incorrect spectrogram IDs, which led to numerous errors during our initial tests. We explore JavaScript code snippets that facilitate the dynamic fetching and updating of spectrogram data, enhancing the responsiveness of the application. The tutorial also highlights the use of asynchronous functions to manage data loading and error handling more effectively, ensuring that the user interface remains responsive even when dealing with large datasets or slow network conditions. #EEG #DataVisualization #Plotly #ChartJS #BioniChaos #Kaggle #Programming #WebDevelopment #FlaskApp #JavaScript #TechTutorial The tools I develop are available on https://bionichaos.com You can support my work on   / bionichaos   0:00 - Introduction to EEG Data Visualization 0:09 - Problems with Chart.js for Spectrograms 0:22 - Spectrogram Data Issues 0:33 - Exploring Chart.js Limitations 0:42 - Switching to Plotly.js 1:05 - Sample Code and Flask Application 1:29 - Troubleshooting HTML Integration 2:05 - Introducing Plotly for Spectrograms 2:32 - Plotting Multiple Spectrograms 3:07 - Parsing Frequency Data 3:17 - Debugging Spectrogram Loading Issues 4:05 - Reviewing EEG Spectrogram Code 5:04 - Role of GitHub Copilot 6:05 - Tool on BioniChaos.com 7:00 - Parsing Sub ID for EEG Data 8:12 - Addressing Spectrogram Errors 9:01 - Real-Time Debugging and Fixes 10:01 - Functionality of Fetch and Display 10:20 - Initial Data Fetch and Plotting 11:12 - Reviewing Chart.js Elements 12:01 - Switching Completely to Plotly.js 13:03 - Debugging Plotly Integration 14:04 - Finalizing Spectrogram Display 15:00 - Ensuring Accurate Frequency Mapping 16:00 - Reviewing Data Sample Rates 17:00 - Addressing Spectrogram ID Errors 18:00 - Final Thoughts on Chart.js vs. Plotly.js 19:00 - Inviting Viewers to BioniChaos.com 20:00 - Reviewing Abnormal EEG Samples 21:00 - Automating Data Validation 22:00 - Spectrograms and Raw EEG Correlation 23:00 - Handling Data with Missing Labels 24:00 - Final Debugging Steps 25:00 - Checking Sampling Rates and Frequencies 26:00 - Finalizing Code Adjustments 27:00 - Verifying Spectrogram Outputs 28:00 - Final Thoughts and Conclusion