Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair

In this study, design and implementation of a multi sensor based brain computer interface for disabled and/or elderly people is proposed. Developed system consists of a wheelchair, a high-power motor controller card, a Kinect camera, electromyogram (EMG) and electroencephalogram (EEG) sensors and a computer. The Kinect sensor is installed on the system to provide safe navigation for the system. Depth frames, captured by the Kinect's infra-red (IR) camera, are processed with a custom image processing algorithm in order to detect obstacles around the wheelchair. A Consumer grade EMG device (Thalmic Labs) was used to obtain eight channels of EMG data. Four different hand movements: Fist, release, waving hand left and right are used for EMG based control of the robotic wheelchair. EMG data is first classified using artificial neural network (ANN), support vector machines and random forest schemes. The class is then decided by a rule-based scheme constructed on the individual outputs of the three classifiers. EEG based control is adopted as an alternative controller for the developed robotic wheelchair. A wireless 14-channels EEG sensor (Emotiv Epoch) is used to acquire real time EEG data. Three different cognitive tasks: Relaxing, math problem solving, text reading are defined for the EEG based control of the system. Subjects were asked to accomplish the relative cognitive task in order to control the wheelchair. During experiments, all subjects were able to control the robotic wheelchair by hand movements and track a pre-determined route with a reasonable accuracy. The results for the EEG based control of the robotic wheelchair are promising though vary depending on user experience.