Lightweight Deep Learning Models for Detection of Neurological Disorders Using EEG Signals

This project develops lightweight EEG-based models for detecting Epilepsy, Alzheimer’s Disease, and Parkinson’s Disease using separate models. Each uses a Base Network with an Error-Correction Network (ECN) to improve accuracy while keeping computation low, ensuring efficient, scalable, and reliable performance across diverse EEG datasets.