Abstract
In this paper, we present a novel motor imagery classification method in electroencephalogmphy (EEG)-based Bmin-Computer lnterfaces (BCIs) using forced whitened sampIe covariance matdces as features. The proposed method performs a constant-forcing to the weaker sources of covadance matrices before a whitening process to prevent amplifications of noise sources which have small power relative to class relevant sources. Expedmental results show the improved accuracy in comparison with a classification without forced whitening process.
| Original language | English |
|---|---|
| Title of host publication | 5th International Winter Conference on Brain-Computer Interface, BCI 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 66-68 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781509050963 |
| DOIs | |
| Publication status | Published - 2017 Feb 16 |
| Event | 5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of Duration: 2017 Jan 9 → 2017 Jan 11 |
Other
| Other | 5th International Winter Conference on Brain-Computer Interface, BCI 2017 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon Province |
| Period | 17/1/9 → 17/1/11 |
Keywords
- Brain-computer interface (BCI)
- Classification
- Sensor covariance matrix
- Supporting vector machine (SVM)
- Whitening matrix
ASJC Scopus subject areas
- Signal Processing
- Human-Computer Interaction
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Cite this
Shin, H., & Chung, W. (2017). Brain computer interface approach using sensor covariance matrix with forced whitening. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017 (pp. 66-68). [7858161] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2017.7858161