Real Time Control System for Wheel Chair of Disabled People Using EEG Signal

This paper introduces a real time control system of disabled electric wheel chair based on using electroencephalography (EEG) data and make sense of it. The main goal is increasing the accuracy rate of the brain control system by using a machine learning algorithm called back propagation (BP). There are a lot of EEG samples taken from a lot of people who all had healthy brains so that they can pick the best EEG channel that can be used as a learning input. After classification, the channels AF3 and AF4 are the most important EEG channels in (Emotiv). For directional classification, AF3 is the most important channel for left and AF4 is the most important channel for right. A microcontroller called an Arduino is used to control the movement of the wheels, and our software is used to do this. There is now a brain-controlled electric wheelchair with better and more accurate EEG classification as a result of this study.