Optimizing EEG Data Visualization: JavaScript, Plotly vs. Chart.js, and Python Backend
In this video, I dive deep into EEG waveform visualization and how we can make it more responsive. I explore the challenges of using heavy processing that slows down both the CPU and GPU, and the tools that can help alleviate these issues, like Chart.js and Plotly. We’ll go through how JavaScript handles EEG data, its interactive visualization capabilities, and the comparison between using Python with Plotly Express for local data analysis versus web-based solutions like Chart.js.
I also walk through how we’re converting the current Python backend into a Flask application, moving most functionality to the front-end JavaScript to ensure smooth, responsive interactions. Plus, I share the advantages and disadvantages of different libraries for EEG visualization and performance improvements we’ve implemented.
You can check out the code and try it for yourself on my website! Any feedback is appreciated as I continue to refine the tool.
🔗 Explore the EEG Viewer: https://bionichaos.com/EEGView
Key highlights:
Chart.js vs. Plotly for EEG visualization
Adaptive sampling for performance improvement
Converting Python code into a Flask application
How to optimize data handling for real-time interaction
I hope you find this deep dive useful, and feel free to leave comments or suggestions. If you’d like to support this project or my work, you can check out more on BioniChaos.
#EEGVisualization #DataScience #PythonFlask #JavaScript #Plotly #ChartJS #EEGAnalysis #WebDevelopment #Neuroscience #BioniChaos
0:00 Introduction to EEG waveform visualization
0:09 System performance issues with EEG data
0:18 Discussing Chart.js vs. other visualization options
0:36 Handling indexing issues in the EEG data
0:55 Using JavaScript and GitHub Copilot for code generation
1:14 Explanation of the index range and calculation issues
1:32 Trying solutions locally with the Python backend
1:54 Issues with window resizing and data display
2:12 Overview of the JavaScript code and Chart.js configuration
2:43 Adaptive sampling and zoom level handling
3:02 Comparing Plotly and Chart.js performance
3:41 Introduction to Pandas and Plotly Express for Python
4:22 Differences between web-based and local data analysis
5:05 Planning to convert the code into a Flask application
6:00 Visualization options for multiple EEG channels
6:29 Performance monitoring and improving responsiveness
7:01 Exploring how to share EEG data visualizations
7:37 Optimizing the front-end JavaScript code
8:43 Benefits of offloading tasks to JavaScript
9:19 Addressing data fetching and production checks
9:39 Comparing performance: Python vs. JavaScript
10:18 Updating HTML and fixing data issues
11:00 Planning for finalizing the Flask application
12:00 Adjusting the layout and improving user interaction
12:56 Final thoughts and next steps for improving performance
13:17 Outro and improvements to come
The tools I develop are available on https://bionichaos.com
You can support my work on / bionichaos
You can join this channel to get access to perks:
/ @bionichaos
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