Abstract
In order to deal with the classification for multi-class motor imagery(MI) tasks, a novel approach was presented in this paper. It is different from classical methods which classified the MI task with time-frequency analysis on EEG signals. It employs the brain function network(BFN) as a new characteristic to describe MI tasks. The BFN enlarges the features with respect to traditional time-frequency methods. Unlike analysis of statistical parameters of network such as average clustering coefficient (C) and the average pathlength (L), the proposed method employed spectral decomposition performing on BFNs, and considered the eigenvalue vector of threshold matrix as features for classification by SVM. Hence, it is speedy enough to meet the requirement of real-time in BCI-based application systems. The result of experiment demonstrates that proposed method can achieve satisfied accuracy of classification on multi-class MI tasks.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011 |
| Pages | 850-853 |
| Number of pages | 4 |
| Volume | 2 |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011 - Shanghai, China Duration: Oct 15 2011 → Oct 17 2011 |
Other
| Other | 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011 |
|---|---|
| Country | China |
| City | Shanghai |
| Period | 10/15/11 → 10/17/11 |
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Keywords
- BCI
- brain function network
- classification
- motor imagery
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
- Health Information Management
Cite this
Spectral analysis of brain function network for the classification of motor imagery tasks. / Kong, Wanzeng; Guo, Xinwei; Zhao, Xinxin; Wei, Daming; Hu, Sanqing; Dai, Guojun; Vecchiato, Giovanni; Babiloni, Fabio.
Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011. Vol. 2 2011. p. 850-853 6098491.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Spectral analysis of brain function network for the classification of motor imagery tasks
AU - Kong, Wanzeng
AU - Guo, Xinwei
AU - Zhao, Xinxin
AU - Wei, Daming
AU - Hu, Sanqing
AU - Dai, Guojun
AU - Vecchiato, Giovanni
AU - Babiloni, Fabio
PY - 2011
Y1 - 2011
N2 - In order to deal with the classification for multi-class motor imagery(MI) tasks, a novel approach was presented in this paper. It is different from classical methods which classified the MI task with time-frequency analysis on EEG signals. It employs the brain function network(BFN) as a new characteristic to describe MI tasks. The BFN enlarges the features with respect to traditional time-frequency methods. Unlike analysis of statistical parameters of network such as average clustering coefficient (C) and the average pathlength (L), the proposed method employed spectral decomposition performing on BFNs, and considered the eigenvalue vector of threshold matrix as features for classification by SVM. Hence, it is speedy enough to meet the requirement of real-time in BCI-based application systems. The result of experiment demonstrates that proposed method can achieve satisfied accuracy of classification on multi-class MI tasks.
AB - In order to deal with the classification for multi-class motor imagery(MI) tasks, a novel approach was presented in this paper. It is different from classical methods which classified the MI task with time-frequency analysis on EEG signals. It employs the brain function network(BFN) as a new characteristic to describe MI tasks. The BFN enlarges the features with respect to traditional time-frequency methods. Unlike analysis of statistical parameters of network such as average clustering coefficient (C) and the average pathlength (L), the proposed method employed spectral decomposition performing on BFNs, and considered the eigenvalue vector of threshold matrix as features for classification by SVM. Hence, it is speedy enough to meet the requirement of real-time in BCI-based application systems. The result of experiment demonstrates that proposed method can achieve satisfied accuracy of classification on multi-class MI tasks.
KW - BCI
KW - brain function network
KW - classification
KW - motor imagery
UR - http://www.scopus.com/inward/record.url?scp=84862971136&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862971136&partnerID=8YFLogxK
U2 - 10.1109/BMEI.2011.6098491
DO - 10.1109/BMEI.2011.6098491
M3 - Conference contribution
AN - SCOPUS:84862971136
SN - 9781424493524
VL - 2
SP - 850
EP - 853
BT - Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011
ER -