A multi-class tactile brain-computer interface based on stimulus-induced oscillatory dynamics
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Date
2017-07-24
Authors
Yao, Lin
Chen, Mei Lin
Sheng, Xinjun
Mrachacz-Kersting, Natalie
Zhu, Xiangyang
Farina, Dario
Jiang, Ning
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
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 (SS-B), 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 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 accuracy of 59.4±7.3%. These results show that the proposed system is a promising new paradigm for multi-class BCI.
Description
© 2017 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Yao, L., Chen, M. L., Sheng, X., Mrachacz-Kersting, N., Zhu, X., Farina, D., & Jiang, N. (2017). A multi-class tactile brain-computer interface based on stimulus-induced oscillatory dynamics. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1–1. https://doi.org/10.1109/TNSRE.2017.2731261
Keywords
Electroencephalography, Wrist, Vibrations, Time-frequency analysis, Visualization, Brain-computer interfaces, Indexes