# 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