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 |
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Keywords
- Brain-computer interface (BCI)
- Classification
- Sensor covariance matrix
- Supporting vector machine (SVM)
- Whitening matrix
ASJC Scopus subject areas
- Signal Processing
- Human-Computer Interaction
Cite this
Brain computer interface approach using sensor covariance matrix with forced whitening. / Shin, Hyuksoo; Chung, Wonzoo.
5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 66-68 7858161.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Brain computer interface approach using sensor covariance matrix with forced whitening
AU - Shin, Hyuksoo
AU - Chung, Wonzoo
PY - 2017/2/16
Y1 - 2017/2/16
N2 - 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.
AB - 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.
KW - Brain-computer interface (BCI)
KW - Classification
KW - Sensor covariance matrix
KW - Supporting vector machine (SVM)
KW - Whitening matrix
UR - http://www.scopus.com/inward/record.url?scp=85016023525&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016023525&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2017.7858161
DO - 10.1109/IWW-BCI.2017.7858161
M3 - Conference contribution
AN - SCOPUS:85016023525
SP - 66
EP - 68
BT - 5th International Winter Conference on Brain-Computer Interface, BCI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -