We have developed a movement assistive system using EEG-controlled functional electric stimulation system. Users’ movement intentions are recognized by brain computer interface, implemented by deep learning network, and the detected users’ intentions are then translated into commands to trigger a functional electric stimulation (FES) device to activate users’ particular movements, such as grasping, hand raising, holding a cup, etc. In our study, we have designed our own wireless dry-electrode EEG system and our own FES system.

The combination of BCI and FES is a novel idea. We detected motor intention from movement-induced EEG features to drive a FES for rehabilitation purposes. We are able to discriminate more than five motor intentions. The BCI requires the help of artificial intelligence and huge computation for accurate detection. Our system utilizes innovative dry-electrode for EEG measurements. The dry-electrode EEG design can increase the safety reliability and reduce the backflow of FES currents.

Our proposed system not only aims to help the mobility of disabled patients. The main purpose is to help the disabled patients activate their muscles in accordance with their intentions. The system enables paralyzed or disabled patients to control their disabled limbs, prevent muscle atrophy, and achieve the purposes of rehabilitation and mobility aids. The idea of combining BCI using dry-electrode EEG and FES can help disabled patients control their disabled limbs, provide a healthy life, regain self-esteem and satisfy their self-confidence.