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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on neural systems and rehabilitation engineering Jg. 26; H. 1; S. 3 - 10
Hauptverfasser: Yao, Lin, Chen, Mei Lin, Sheng, Xinjun, Mrachacz-Kersting, Natalie, Zhu, Xiangyang, Farina, Dario, Jiang, Ning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2018
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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