Abstract
In this paper, we explore locally generated cross-channel-region CSP features to improve motor imagery classification in EEG-based BCIs. We set several clustered sub-channel regions covering the entire measured channels and extract CSP features by cross-combining the sub-channel regions with each single channel. The features generated by this cross-channel-region combinations have regional information on sensor space for motor imagery and can be used to improve classification accuracy when fed to LS-SVM classifier. The performance improvement of the proposed algorithm is verified by simulations.
Original language | English |
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Title of host publication | 2018 6th International Conference on Brain-Computer Interface, BCI 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-4 |
Number of pages | 4 |
ISBN (Electronic) | 9781538625743 |
DOIs | |
Publication status | Published - 2018 Mar 9 |
Event | 6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of Duration: 2018 Jan 15 → 2018 Jan 17 |
Publication series
Name | 2018 6th International Conference on Brain-Computer Interface, BCI 2018 |
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Volume | 2018-January |
Other
Other | 6th International Conference on Brain-Computer Interface, BCI 2018 |
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Country/Territory | Korea, Republic of |
City | GangWon |
Period | 18/1/15 → 18/1/17 |
Bibliographical 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).
Keywords
- Brain-Computer Interfaces (BCIs)
- Common Spatial Pattern (CSP)
- cross-channel-region combinations
- electroencephalography (EEG)
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Behavioral Neuroscience