A Multi-Class Tactile Brain-Computer Interface Based on Stimulus-Induced Oscillatory Dynamics

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 att...

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Vydáno v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 26; číslo 1; s. 3 - 10
Hlavní autoři:	Yao, Lin, Chen, Mei Lin, Sheng, Xinjun, Mrachacz-Kersting, Natalie, Zhu, Xiangyang, Farina, Dario, Jiang, Ning
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.01.2018
Témata:	Algorithms Attention - physiology Brain-computer interfaces Brain-Computer Interfaces - classification Discrimination (Psychology) Electroencephalography Equipment Design Evoked Potentials, Somatosensory Female Hand - innervation Healthy Volunteers Humans Indexes Male Reproducibility of Results selective sensation somatosensory attention stimulus-induced oscillatory dynamics Tactile BCI Time-frequency analysis Touch - physiology Vibrations Visualization Wrist Young Adult
ISSN:	1534-4320, 1558-0210, 1558-0210
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1534-4320 1558-0210 1558-0210
DOI:	10.1109/TNSRE.2017.2731261