TY - GEN
T1 - Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI
AU - Park, Yongkoo
AU - Chung, Wonzoo
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/2
Y1 - 2019/2
N2 - The EEG-based brain-computer interface (BCI) requires removal of irrelevant channels to improve performance. In this paper, we propose the optimal channel selection using EEG channel covariance matrix and cross-combining region. First, the discriminative H channels and target channel are selected by difference of EEG channel covariance matrix between two classes. Second, we configure several sub-channel regions to cover the H channels. Then, we extract FBCSP features from cross-combining regions which are combination of the sub-channel regions and target channel. We select the best one cross-combining region and the optimal channels which are included in selected cross-combining region are finally selected. The features of selected region are used as input of LS-SVM classifier. The simulation results show the performance improvement of proposed method for BCI competition III dataset IVa by comparing the conventional channel selection methods.
AB - The EEG-based brain-computer interface (BCI) requires removal of irrelevant channels to improve performance. In this paper, we propose the optimal channel selection using EEG channel covariance matrix and cross-combining region. First, the discriminative H channels and target channel are selected by difference of EEG channel covariance matrix between two classes. Second, we configure several sub-channel regions to cover the H channels. Then, we extract FBCSP features from cross-combining regions which are combination of the sub-channel regions and target channel. We select the best one cross-combining region and the optimal channels which are included in selected cross-combining region are finally selected. The features of selected region are used as input of LS-SVM classifier. The simulation results show the performance improvement of proposed method for BCI competition III dataset IVa by comparing the conventional channel selection methods.
KW - Brain-Computer Interfaces (BCIs)
KW - EEG channel selection
KW - common spatial pattern (CSP)
KW - motor imagery
UR - http://www.scopus.com/inward/record.url?scp=85068321336&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2019.8737257
DO - 10.1109/IWW-BCI.2019.8737257
M3 - Conference contribution
AN - SCOPUS:85068321336
T3 - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
BT - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Winter Conference on Brain-Computer Interface, BCI 2019
Y2 - 18 February 2019 through 20 February 2019
ER -