title: Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials.
abstract: 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.
authors: Keun-Tae
authors: KT
authors: Kim
authors: Heung-Il
authors: HI
authors: Suk
authors: Seong-Whan
authors: SW
authors: Lee
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods; D018913:Event-Related Potentials, P300 / Q000502:physiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology*; D005260:Female; D005528:Foot / Q000502:physiology; D006225:Hand / Q000502:physiology; D064368:Healthy Volunteers; D006801:Humans; D007092:Imagination; D000069550:Machine Learning; D008297:Male; D009068:Movement; D014910:Wheelchairs; D055815:Young Adult