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.
|Title of host publication||7th International Winter Conference on Brain-Computer Interface, BCI 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|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
|Name||7th International Winter Conference on Brain-Computer Interface, BCI 2019|
|Conference||7th International Winter Conference on Brain-Computer Interface, BCI 2019|
|Country/Territory||Korea, Republic of|
|Period||19/2/18 → 19/2/20|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).
© 2019 IEEE.
- 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)