EEG Signals Classification: Motor Imagery for Driving an Intelligent Wheelchair

The activities pertaining to body control performed by human beings utilize neuromuscular tracts. Tasks' performance such as moving the arms or walking demand the planning of the task to be performed. People diagnosed with clinical conditions such as Amyotrophic Lateral Sclerosis, Spine lesions or Cerebrovascular Accident, for instance, have their neuromuscular tracts damaged. One of the alternatives to bypass that problem is the development of technologies which can partially replace the loss functioning of people with severe motor impairment. The imagination of the movement is considered as a cognitive state which corresponds to the mental simulation of a given motor action. The general aim of this investigative research is to develop a brain-computer based interface for the movement imagination of the left fist, right fist, both fists and both feet in order to control an intelligent wheelchair. The electroencephalography signals were acquired through the database eegmmidb - EEG Motor Movement/Imagery Dataset. Electroencephalography signals samples of 106 individuals were utilized in order to validate the computational model. The proposed model obtained an efficiency of 74,96% in the correct classification of the events related to movement imagination. The developed techniques are promising. The model intends to contribute as a complementation of an improvement towards the mobility of people suffering from severe motor impairment.