Deep Learning-Based EEG Signals Interpretation for Motor Imagery

This paper presents the design and development of a dry-EEG-based motor imagery classification system for a Brain-Computer Interface (BCI)-operated wheelchair, utilizing a deep learning approach. While wet EEG remains prevalent for brain activity recording, its discomfort limits continuous monitoring. Dry EEG offers improved comfort and ease of use, prompting this study to explore its potential. We propose a system that classifies dry-EEG signals into four motor imagery classes. Our objective is to extract meaningful features from the inherently noisy and weak dry-EEG data, thereby enhancing performance for practical BCI applications. A Convolutional Neural Network (CNN) model was developed, and the impact of Principal Component Analysis (PCA) for dimensionality reduction and feature extraction was evaluated. Experimental results demonstrated classification accuracies of 93.33% using raw data and 96.42% after preprocessing the data by PCA, showing the system's effectiveness.