Attention-Controlled Assistive Wrist Rehabilitation Using a Low-Cost EEG Sensor

Abstract

It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist flexion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training: two types of visual guidance, namely, looking at the hand motion shown on a video and looking at the user's own hand had no significant performance difference. A general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure.