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

Project Overview

This project focuses on the development of a lightweight deep learning framework for detecting neurological disorders using electroencephalogram (EEG) signals. The system is designed to achieve a balance between diagnostic accuracy and computational efficiency, enabling deployment on resource-constrained edge devices.

The project targets three major neurological disorders:

• Alzheimer’s Disease
• Parkinson’s Disease
• Epilepsy

Objectives

• Develop efficient deep learning models for EEG-based neurological disorder detection
• Reduce model complexity while maintaining acceptable accuracy
• Evaluate performance in terms of accuracy, inference time, and resource usage
• Enable feasibility for edge and real-time healthcare applications

Key Features

• EEG signal preprocessing and normalization
• Lightweight model architectures (e.g., compact CNN and hybrid models)
• Performance evaluation using standard EEG datasets
• Emphasis on edge deployment constraints such as memory and inference latency

Technologies Used

• Python
• PyTorch / TensorFlow
• NumPy, SciPy
• Scikit-learn
• EEG processing libraries

Repository Structure

├── data/              # EEG datasets or dataset loaders
├── preprocessing/     # EEG preprocessing scripts
├── models/            # Deep learning model implementations
├── training/          # Training and evaluation scripts
├── experiments/       # Experiment configurations and results
├── utils/             # Helper functions
└── README.md