Robotic neurorehabilitation system design for stroke patients

Abstract

In this article, a neurorehabilitation system combining robot-aided rehabilitation with motor imagery–based brain–computer interface is presented. Feature extraction and classification algorithm for the motor imagery electroencephalography is implemented under our brain–computer interface research platform. The main hardware platform for functional recovery therapy is the Barrett Whole-Arm Manipulator. The mental imagination of upper limb movements is translated to trigger the Barrett Whole-Arm Manipulator Arm to stretch the affected upper limb to move along the predefined trajectory. A fuzzy proportional–derivative position controller is proposed to control the Whole-Arm Manipulator Arm to perform passive rehabilitation training effectively. A preliminary experiment aimed at testing the proposed system and gaining insight into the potential of motor imagery electroencephalography-triggered robotic therapy is reported.