Enhancing EEG Signal Analysis: Fuzzy Logic, Real-Time Controls & Color Coding Explained

In this live session, I walk through the latest improvements in our EEG signal analysis tool, focusing on enhancing user experience with fuzzy logic, real-time controls, and color coding for better signal visualization. I explain how we’ve integrated synthetic EEG generation with multiple waveforms, including alpha, beta, delta, and theta waves, and the implementation of artifact control to simulate real-life EEG scenarios.

We also dive into simplifying the interface by removing redundant buttons, adjusting sliders for noise and artifact levels, and incorporating tooltips for an educational touch. You’ll see how these updates make the tool more interactive, educational, and fun for users who want to explore the world of EEG data.

Make sure to check out my website for more tools and live streams on biomedical data visualization!

🔗 Explore more at: bionichaos.com

Got feedback or questions? Leave them in the comments, and I’ll review them in the next live stream. Thanks for tuning in!

#EEGAnalysis #SyntheticEEG #FuzzyLogic #BiomedicalData #SignalProcessing #RealTimeControls #Neuroscience #Brainwaves #LiveCoding #BioniChaos

0:09 – Summary of EEG signal analysis improvements
0:16 – Backend updates: Flask, app.py, and global EEG data management
0:27 – Generating synthetic EEG: Sine wave, noise, and EMG artifacts
0:34 – Fuzzy logic explained: Amplitude, frequency, and artifact risk
0:46 – Fuzzy logic system membership functions and rules
1:04 – Frontend updates: Generating and analyzing EEG signals
1:27 – Real-time EEG visualization with Chart.js
1:51 – Signal quality: Amplitude, frequency, and artifact risk displayed
2:15 – Automating signal generation and analysis upon page load
2:51 – Handling the "crisp output cannot be calculated" error
4:12 – Reviewing the error sources and rule activation
5:20 – Visualizing fuzzy logic rules and membership functions
6:13 – Improvements: Adding real-time analysis and signal controls
7:20 – Fixing the Chart.js canvas reuse error
8:42 – Debugging frontend and backend integration issues
10:00 – Adjusting signal generation parameters and noise control
12:00 – Interactive sliders for EEG components: Alpha, Beta, Delta, Theta
15:30 – Real-time adjustments for artifact levels and signal quality
18:00 – Reviewing performance and signal behavior
21:00 – Addressing the server load and performance issues
25:00 – Handling undefined errors and plugin issues in the browser
30:00 – Breaking down complex visualizations and debugging
35:00 – Refining signal generation and reducing clutter in visualizations
40:00 – Preparing to split EEG generation and fuzzy logic tools
45:00 – Further improving signal visualization: Noise, amplitude, and frequency
50:00 – Reviewing user interface design and feedback on mobile
55:00 – Wrapping up: Next steps and feedback request

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