Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI

Yongkoo Park; Wonzoo Chung

doi:10.1109/IWW-BCI.2019.8737257

Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI

Yongkoo Park, Wonzoo Chung

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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	https://doi.org/10.1109/IWW-BCI.2019.8737257
Publication status	Published - 2019 Feb
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

Keywords

Brain-Computer Interfaces (BCIs)
EEG channel selection
common spatial pattern (CSP)
motor imagery

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Signal Processing
Neuroscience (miscellaneous)

Access to Document

10.1109/IWW-BCI.2019.8737257

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Park, Y., & Chung, W. (2019). Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI. In 7th International Winter Conference on Brain-Computer Interface, BCI 2019 [8737257] (7th International Winter Conference on Brain-Computer Interface, BCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2019.8737257

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

Park, Y & Chung, W 2019, Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI. in 7th International Winter Conference on Brain-Computer Interface, BCI 2019., 8737257, 7th International Winter Conference on Brain-Computer Interface, BCI 2019, Institute of Electrical and Electronics Engineers Inc., 7th International Winter Conference on Brain-Computer Interface, BCI 2019, Gangwon, Korea, Republic of, 19/2/18. https://doi.org/10.1109/IWW-BCI.2019.8737257

Park Y, Chung W. Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI. In 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). https://doi.org/10.1109/IWW-BCI.2019.8737257

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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.",

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author = "Yongkoo Park and Wonzoo Chung",

note = "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).; 7th International Winter Conference on Brain-Computer Interface, BCI 2019 ; Conference date: 18-02-2019 Through 20-02-2019",

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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.

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