EEG Cognitive State Classification
Objective: Analysed EEG data to classify cognitive states using advanced deep learning techniques. ● Data Loading: Loaded EEG data from the PhysioNet repository.
● Power Spectral Density (PSD) Analysis:
○ Calculated band-wise PSD for resting and task states.
○ Focused on Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz), Beta (12-30 Hz), and Gamma (30-100 Hz) bands.
○ Compared the PSDs of the two states and summarized the findings.
● Deep Learning Classification:
○ Implemented binary classification using the EEGNet model.
○ Trained and validated the models using the provided dataset.
○ Evaluated the models using accuracy, precision, recall, and F1-score metrics.
● Technology used: Python, MNE Libraries, TensorFlow & PyTorch, EEGNet