| # 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 | |