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Feature bands study of MI signals based on FBCSP for CNN:
, In:
2022 18th International Conference on Computational Intelligence and Security (CIS)
,
Wang, Liubin
;
Deng, Xiaofang
;
Jiang, Qiuxiang
... - p. 157-161 , 2022
Link:
https://doi.org/10.1109/CIS58238.2022.00040
RT T1 <cTitle>2022 18th International Conference on Computational Intelligence and Security (CIS)</cTitle>: T1 <Title>Feature bands study of MI signals based on FBCSP for CNN</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10091336&Exemplar=1&LAN=DE A1 Wang, Liubin A1 Deng, Xiaofang A1 Jiang, Qiuxiang A1 Zheng, Lin A1 Yang, Chao A1 Mo, Liuqiaoyu YR 2022 K1 Training K1 Time-frequency analysis K1 Computational modeling K1 Brain modeling K1 Feature extraction K1 Electroencephalography K1 Data models K1 Brain-Computer Interface (BCI) K1 Deep Learning (DL) K1 Convolutional Neural Network (CNN) K1 Motor Imagery Signal (MI) K1 Feature Bands SP 157 OP 161 LK http://dx.doi.org/https://doi.org/10.1109/CIS58238.2022.00040 DO https://doi.org/10.1109/CIS58238.2022.00040 SF ELIB - SuUB Bremen
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