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EEG Cognitive Load Classifier

Overview

Cognitive load assessment is vital for understanding human attention, effort, and fatigue during tasks. This project classifies mental workload (High, Medium, Low) using non-invasive EEG signals, offering an objective alternative to traditional subjective surveys. EEG band powers (Alpha, Beta, Theta) are extracted and used in simple machine learning models for classification.

Objectives

  • Classify mental workload levels (High, Medium, Low) using EEG analysis (Alpha, Beta, Theta bands).
  • Automate EEG data extraction, spectral analysis, and classification using Python.
  • Visualize cognitive load trends across subjects and sessions.

Methodology

Pipeline Steps

Signal Acquisition

  • Uses publicly available EEG datasets (e.g., Physionet).
  • No hardware required; raw EEG files are input.

Preprocessing

  • Band-pass filtering removes noise and isolates Alpha, Beta, Theta frequency bands.
  • Uses the MNE Python library for EEG data loading and preprocessing.

Feature Extraction

  • Computes Power Spectral Density (PSD) using Welch’s method.
  • Integrates PSD for each frequency band (Alpha, Beta, Theta) using SciPy.

Classification

  • Extracted band powers classified into High, Medium, or Low cognitive load.
  • Classification is based on band power thresholds.

Visualization

  • Plots EEG band powers for trend analysis across subjects using Matplotlib.

Tools Used

  • Python: Programming
  • MNE: EEG data loading and preprocessing
  • SciPy: Signal processing
  • Matplotlib: Visualization
  • Welch’s Method: Band power estimation

Input & Output

Aspect Description
Input Raw EEG files (Physionet dataset or similar)
Processing Channel extraction, PSD computation, Alpha/Beta/Theta band integration
Terminal Output Band power values and workload classification per file
Plot Output Visual trend comparison (band power, across subjects)

Installation

Clone this repository: git clone https://github.com/ramkumar27072006/EEG-Cognitive-Load-Classifier.git cd EEG-Cognitive-Load-Classifier

text

Install required packages: pip install -r requirements.txt

text (You may need to create or update requirements.txt with: mne, scipy, matplotlib, numpy.)

Usage

Prepare EEG Data

  • Download raw EEG dataset (e.g., from Physionet).
  • Place files in the designated /data directory.

Run the Classifier python main.py --input data/<eeg_file>

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  • Outputs terminal classification (workload: High, Medium, Low) per file.
  • Generates band power plots for visual analysis.

Configuration

  • Script parameters (e.g., frequency bands, classification thresholds) can be adjusted in the configuration section of the code.

Results

  • Terminal output: Shows Alpha, Beta, Theta values for each file and classified cognitive load.
  • Graphs: Band power across subjects for trend analysis.

Applications

  • Adaptive learning systems
  • Mental workload monitoring in critical environments
  • Stress detection in workplaces
  • Mindfulness/meditation state tracking

Credits

Team: Pragalya M, Ramkumar R, Youvashree K
Based on Physionet EEG datasets.

License

MIT License