TY - JOUR
T1 - Commanding a brain-controlled wheelchair using steady-state somatosensory evoked potentials
AU - Kim, Keun Tae
AU - Suk, Heung Il
AU - Lee, Seong Whan
N1 - Funding Information:
Manuscript received May 18, 2015; revised October 4, 2015, March 15, 2016, and May 26, 2016; accepted June 5, 2016. Date of publication August 3, 2016; date of current version March 6, 2018. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2015R1A2A1A05001867) S.-W. Lee is the corresponding author.
Publisher Copyright:
© 2001-2011 IEEE.
PY - 2018/3
Y1 - 2018/3
N2 - 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.
AB - 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.
KW - Brain-machine interfaces (BMIs)
KW - brain-controlled wheelchair
KW - electroencephalography (EEG)
KW - motor imagery (MI)
KW - steady-state somatosensory evoked potential (SSSEP)
UR - http://www.scopus.com/inward/record.url?scp=85032641421&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2016.2597854
DO - 10.1109/TNSRE.2016.2597854
M3 - Article
C2 - 27514060
AN - SCOPUS:85032641421
SN - 1534-4320
VL - 26
SP - 654
EP - 665
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 3
ER -