Fuzzy Logic & EEG Simulation

Overview

This interactive application simulates Electroencephalography (EEG) brain waves and demonstrates how Fuzzy Logic can be applied to classify human cognitive states. Unlike standard boolean logic (True/False), Fuzzy Logic deals with degrees of truth, making it highly suitable for noisy, continuous biological signals like EEG.

How to Use

• Interactive Canvas: The top panel shows the raw simulated EEG signal. The bottom left shows a frequency spectrum (bands). The bottom right visualizes the Fuzzy Logic membership sets and inference.
• State Buttons: Click the buttons (Deep Sleep, Relaxed, etc.) to simulate different mental states. Watch how the frequency composition changes.
• Sonification: Click "Sound Off" to toggle audio. The system maps the dominant EEG frequencies to a musical chord (Delta=Root, Theta=Third, Alpha=Fifth, Beta=Seventh, Gamma=Octave). The volume of each note reflects the power of that brainwave band.
• Demo Mode: Click "Play Demo" to automatically cycle through states. Demo mode also starts automatically after 10 seconds of inactivity. Click anywhere to stop it.

Technical Details: EEG & Frequencies

An EEG signal is a complex waveform representing electrical brain activity. We decompose this signal into distinct frequency bands:

• Delta (0.5 - 4 Hz): Dominant in deep, dreamless sleep.
• Theta (4 - 8 Hz): Present during drowsiness, light sleep, or deep meditation.
• Alpha (8 - 13 Hz): Associated with relaxed, calm, lucid states. Often peaks when eyes are closed.
• Beta (13 - 30 Hz): Active, busy, or anxious thinking and active concentration.
• Gamma (30+ Hz): High-level cognitive processing, peak focus, and information synthesis.

Technical Details: Fuzzy Logic Engine

In this simulation, we implement a Fuzzy Inference System (FIS) in JavaScript:

1. Fuzzification: We take the crisp normalized power (0.0 to 1.0) of the Alpha and Beta bands. We pass these values through membership functions (triangles). For example, an Alpha power of 0.6 might be 20% "Medium" and 80% "High".
2. Rule Evaluation: We apply logical rules. Example: IF Alpha is High AND Beta is Low THEN State is Relaxed. In fuzzy logic, AND is typically computed as the minimum of the two fuzzy values.
3. Defuzzification: We aggregate the results of the rules to compute a crisp "Cognitive Alertness" score (shown as the gauge on the bottom right), using a simplified weighted average.

Future Directions

In real-world Brain-Computer Interfaces (BCIs), Fuzzy Logic is often combined with Neural Networks (Neuro-Fuzzy Systems like ANFIS) to automatically tune the membership functions based on user-specific training data. This handles inter-subject variability, as one person's "High Alpha" might look different from another's.