TY - GEN
T1 - A multi-scale EEGNet for cross-subject RSVP-based BCI system
AU - Wang, Xuepu
AU - Lin, Yanfei
AU - Tan, Ying
AU - Guo, Rongxiao
AU - Gao, Xiaorong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the cross-subject classification task, a subject-agnostic model is trained for the classification task of other subjects, according to the prior knowledge from EEG data of some subjects. It is one of the challenges for ERP classification in the RSVP-based BCI system. So far, convolutional neural networks (CNNs) for RSVP classification only use a fixed-size kernel for each layer to extract features in the temporal domain, which limits the ability of the network to detect ERP. In this work, a multi-scale EEGNet model (MS-EEGNet) for cross-subject RSVP classification task was proposed, which adopted parallel convolution layers with multi-scale kernels to extract discrimination information in the temporal domain, and increased the robustness of the model. The proposed model was used for the BCI Controlled Robot Contest in the World Robot Contest 2022 and achieved good results. The UAR of the A and B datasets got 0.493 and 0.528, respectively. Compared with other CNN algorithms including EEGNet and PLNet, the proposed model had better classification performance.
AB - In the cross-subject classification task, a subject-agnostic model is trained for the classification task of other subjects, according to the prior knowledge from EEG data of some subjects. It is one of the challenges for ERP classification in the RSVP-based BCI system. So far, convolutional neural networks (CNNs) for RSVP classification only use a fixed-size kernel for each layer to extract features in the temporal domain, which limits the ability of the network to detect ERP. In this work, a multi-scale EEGNet model (MS-EEGNet) for cross-subject RSVP classification task was proposed, which adopted parallel convolution layers with multi-scale kernels to extract discrimination information in the temporal domain, and increased the robustness of the model. The proposed model was used for the BCI Controlled Robot Contest in the World Robot Contest 2022 and achieved good results. The UAR of the A and B datasets got 0.493 and 0.528, respectively. Compared with other CNN algorithms including EEGNet and PLNet, the proposed model had better classification performance.
KW - classification
KW - cross-subject
KW - electroencephalogram
KW - rapid serial visual presentation
UR - http://www.scopus.com/inward/record.url?scp=85146247303&partnerID=8YFLogxK
U2 - 10.1109/CISP-BMEI56279.2022.9980258
DO - 10.1109/CISP-BMEI56279.2022.9980258
M3 - Conference contribution
AN - SCOPUS:85146247303
T3 - Proceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
BT - Proceedings - 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
A2 - Chen, Xin
A2 - Cao, Lin
A2 - Li, Qingli
A2 - Wang, Yan
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2022
Y2 - 5 November 2022 through 7 November 2022
ER -