Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538681169 |
| DOIs | |
| Publication status | Published - 2019 Feb 1 |
| Event | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 - Gangwon, Korea, Republic of Duration: 2019 Feb 18 → 2019 Feb 20 |
Publication series
| Name | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
|---|
Conference
| Conference | 7th International Winter Conference on Brain-Computer Interface, BCI 2019 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon |
| Period | 19/2/18 → 19/2/20 |
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Keywords
- Brain-Computer Interfaces (BCIs)
- common spatial pattern (CSP)
- EEG channel selection
- motor imagery
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing
- Neuroscience (miscellaneous)
Cite this
Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI. / Park, Yongkoo; Chung, Wonzoo.
7th International Winter Conference on Brain-Computer Interface, BCI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8737257 (7th International Winter Conference on Brain-Computer Interface, BCI 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI
AU - Park, Yongkoo
AU - Chung, Wonzoo
PY - 2019/2/1
Y1 - 2019/2/1
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 - common spatial pattern (CSP)
KW - EEG channel selection
KW - motor imagery
UR - http://www.scopus.com/inward/record.url?scp=85068321336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.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.
ER -