Name
Title | ||
|---|---|---|
An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers |
Abstract | ||
|---|---|---|
An efficient implementation of a multi-class motor imagery (MI) brain computer interface (BCI) classification scheme is presented in this work. The proposed method uses the common spatial pattern (CSP) and filter bank CSP (FBCSP) algorithms, with both one versus all (OVA) and one versus one (OVO) approach for multi-class extension. Mutual information (MInf) based feature selection algorithm has been used to obtain the features to train different linear discriminant analysis (LDA) classifiers. To improve the performance, the outputs of these classifiers are combined using two statistical methods: the mode of the OVA and OVO classifiers, and the more sophisticated Dempster-Shafer (DS) theory. The method has been evaluated on the 4-class MI dataset (BCI competition IV 2a), and the results showed that it has outperformed the winner of the competition (maximum kappa value of 0.593 vs 0.569). The proposed method proved the benefits of combining classifiers with appropriate techniques. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TENCON.2019.8929345 | TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) |
Keywords | Field | DocType |
Brain Computer Interface,Multi-class Motor Imagery,Common Spatial Pattern,Mutual Information,Dempster-Shafer theory | Feature selection,Pattern recognition,Computer science,Filter bank,Brain–computer interface,Fusion,Electronic engineering,Feature extraction,Artificial intelligence,Mutual information,Linear discriminant analysis,Motor imagery | Conference |
ISSN | ISBN | Citations |
2159-3442 | 978-1-7281-1896-3 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paula Sánchez López | 1 | 0 | 0.34 |
| Helle K. Iversen | 2 | 0 | 0.34 |
| Sadasivan Puthusserypady | 3 | 181 | 27.49 |