Robust Analysis of Motor Imagery From Brain Signals for a BCI-Controlled Virtual Reality System to Aid Paralysis Patients

Abstract

The contribution of the brain-computer interface (BCI) ranges from prevention of disease to neuronal control for disabled peoples. BCI-controlled virtual reality system is a potentially important new assistive technology area to aid various physically disable people (i.e., paralyzed people) by monitoring brain activity and translating desired signal features to operate external devices. This research used motor imagery achieved from EEG data implicating three main phases (i.e., preprocessing, features extraction, and classification of brain signals). This research used linear discriminant analysis (LDA) classifier to achieve decision boundary between left hand and right hand imagination. In this context, motor imagery-based EEG data was segmented and classified to be used as a controller for BCI. Experimental results reflect the significant impact of various classifiers and is expected to aid paralyzed people in converting their imagination into reality.