Alzheimer’s Detection Using EEG Data with MOMENT Model

Project Date: 2024

Developed a CNN-Transformer model to detect Alzheimer’s patterns from EEG data.

This project focuses on leveraging a CNN-Transformer architecture, specifically the MOMENT model, for high-accuracy detection of Alzheimer’s disease indicators within EEG data. The model enhances feature extraction from key EEG channels, tackling noise in the signal to improve reliability and clinical applicability. The project uses Python for implementation and integrates deep learning techniques, including Convolutional Neural Networks and Transformers, to handle complex EEG patterns effectively.

GitHub Repository