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 |
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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 |
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Country/Territory | 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