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EEG-Based Biometric Identification Model (Autoencoder + CNN)
This model implements a hybrid architecture combining an Autoencoder for feature extraction and a Convolutional Neural Network (CNN) for classification of EEG signals. It is designed for biometric identification using spectrogram-transformed EEG data.
Model Overview
- Input: Spectrograms generated from EEG signals.
- Architecture:
- Autoencoder: Compresses high-dimensional spectrogram data into compact latent representations.
- CNN Classifier: Learns patterns from either raw spectrograms or encoded features for classification.
- Training Dataset: Public EEG Motor Movement/Imagery Dataset (BCI2000), including signals from 109 subjects across 14 tasks.
Performance
The combined Autoencoder + CNN approach achieves significantly improved classification accuracy compared to baseline CNN-only models, with performance metrics including:
- Accuracy: Up to 99.6%
- F1 Score: High across all subject classes
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