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1
Taming fNIRS-based BCI Input for Better Calibration and Bro..:
, In:
The 34th Annual ACM Symposium on User Interface Software and Technology
,
Wang, Liang
;
Huang, Zhe
;
Zhou, Ziyu
... - p. 179-197 , 2021
Link:
https://dl.acm.org/doi/10.1145/3472749.3474743
RT T1 <cTitle>The 34th Annual ACM Symposium on User Interface Software and Technology</cTitle>: T1 <Title>Taming fNIRS-based BCI Input for Better Calibration and Broader Use</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3474743&Exemplar=1&LAN=DE A1 Wang, Liang A1 Huang, Zhe A1 Zhou, Ziyu A1 McKeon, Devon A1 Blaney, Giles A1 Hughes, Michael C. A1 Jacob, Robert J. K. PB ACM YR 2021 K1 BCI K1 Brain-Computer Interface K1 cognitive workload K1 data augmentation K1 fNIRS K1 implicit interfaces K1 machine learning K1 n-back task K1 near-infrared spectroscopy K1 neural networks K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 179 OP 197 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3472749.3474743 DO https://dl.acm.org/doi/10.1145/3472749.3474743 SF ELIB - SuUB Bremen
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