EEG controlled remote robotic system from motor imagery classification

Abstract

This paper aims at laying a foundation towards the development of a robust platform for efficient control of the motion of autonomous mobile robots. Electroencephalographic (EEG) signals liberated during motor imagery of a human controller have been used to design the control mechanism. The proposed scheme can find widespread applications in the defense sector as secrecy of generated commands can be maintained efficiently. With decoding of the brain signals for various limb movements being a major area of research for EEG based BCI, the paper employs the usage of finger-elbow-shoulder movement classification in addition to the left-right arm movement classification. The proposed system integrates the advantages of high classification accuracy of EEG measurements (more than 80% for both the folds, i.e., Left-Right classification and Finger-Elbow-Shoulder classification) along with a suitable coding technique of liberated control signals for error detection thereby paving the way for an efficient EEG signal encoded robot control system design.