TY - JOUR
T1 - A multi-class tactile brain-computer interface based on stimulus-induced oscillatory dynamics
AU - Yao, Lin
AU - Chen, Mei Lin
AU - Sheng, Xinjun
AU - Mrachacz-Kersting, Natalie
AU - Zhu, Xiangyang
AU - Farina, Dario
AU - Jiang, Ning
PY - 2018/1
Y1 - 2018/1
N2 - We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile-induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L); 2) selective sensation on right hand (SS-R); 3) bilateral selective sensation; and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9 ± 8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3 ± 4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had an accuracy of 75.2 ± 8.3%, with the best discrimination reached for the classes SS-L, SS-R, and SS-S. Finally, four classes were classified with an accuracy of 59.4 ± 7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.
AB - We proposed a multi-class tactile brain-computer interface that utilizes stimulus-induced oscillatory dynamics. It was hypothesized that somatosensory attention can modulate tactile-induced oscillation changes, which can decode different sensation attention tasks. Subjects performed four tactile attention tasks, prompted by cues presented in random order and while both wrists were simultaneously stimulated: 1) selective sensation on left hand (SS-L); 2) selective sensation on right hand (SS-R); 3) bilateral selective sensation; and 4) selective sensation suppressed or idle state (SS-S). The classification accuracy between SS-L and SS-R (79.9 ± 8.7%) was comparable with that of a previous tactile BCI system based on selective sensation. Moreover, the accuracy could be improved to an average of 90.3 ± 4.9% by optimal class-pair and frequency-band selection. Three-class discrimination had an accuracy of 75.2 ± 8.3%, with the best discrimination reached for the classes SS-L, SS-R, and SS-S. Finally, four classes were classified with an accuracy of 59.4 ± 7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.
KW - Journal Article
KW - stimulus-induced oscillatory dynamics
KW - selective sensation
KW - Tactile BCI
KW - somatosensory attention
UR - http://www.scopus.com/inward/record.url?scp=85028948076&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2017.2731261
DO - 10.1109/TNSRE.2017.2731261
M3 - Journal article
C2 - 28742045
SN - 1534-4320
VL - 26
SP - 3
EP - 10
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 1
M1 - 7990164
ER -