A deep learning-based pipeline for automated classification of Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and Cognitively Normal (CN) individuals using EEG data.
This project transforms raw EEG signals into Smoothed Pseudo-Wigner–Ville Distribution (SPWVD) spectrograms and classifies them using an AlexNet-inspired Convolutional Neural Network. Leveraging bipolar montage optimization and ensemble learning, the model achieves up to 91% accuracy on validation data.
- Programming: Python, MATLAB
- Frameworks: TensorFlow, Keras, scikit-learn
- Visualization: Matplotlib, Seaborn
- Model: AlexNet-based CNN
- Preprocessing: SPWVD spectrograms, bipolar EEG montages
- Evaluation: Stratified K-Fold Cross-Validation (10-fold)