Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2018-03, Vol.26 (3), p.654-665
• Main Authors: Kim, Keun-Tae, Suk, Heung-Il, Lee, Seong-Whan
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Brain - physiology; Brain-Computer Interfaces; brain-controlled wheelchair; Brain–machine interfaces (BMIs); Control systems; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Event-Related Potentials, P300 - physiology; Evoked Potentials, Somatosensory - physiology; Female; Foot - physiology; Hand - physiology; Healthy Volunteers; Humans; Imagination; Machine Learning; Male; motor imagery (MI); Movement; Steady-state; steady-state somatosensory evoked potential (SSSEP); Training; Vibrations; Visualization; Wheelchairs; Young Adult
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2016.2597854

In this work, we propose a novel brain-controlled wheelchair, one of the major applications of brain-machine interfaces (BMIs), that allows an individual with mobility impairments to perform daily living activities independently. Specifically, we propose to use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to tactile stimulation of specific frequencies, for a user's intention to control a wheelchair. In our system, a user had three possible commands by concentrating on one of three vibration stimuli, which were attached to the left-hand, right-hand, and right-foot, to selectively control the wheelchair. The three stimuli were associated with three wheelchair commands: turn-left, turn-right, and move-forward. From a machine learning perspective, we also devise a novel feature representation by combining spatial and spectral characteristics of brain signals. In order to validate the effectiveness of the proposed SSSEP-based system, we considered two different tasks: 1) a simple obstacle-avoidance task within a limited time and; 2) a driving task along the predefined trajectory of about 40 m length, where there were a narrow pathway, a door, and obstacles. In both experiments, we recruited 12 subjects and compared the average time of motor imagery (MI) and SSSEP-based controls to complete the task. With the SSSEP-based control, all subjects successfully completed the task without making any collision while four subjects failed it with MI-based control. It is also noteworthy that in terms of the average time to complete the task, the SSSEP-based control outperformed the MI-based control. In the other more challenging task, all subjects successfully reached the target location.