An Efficient Multi-Class MI Based BCI Scheme Using Statistical Fusion Techniques of Classifiers

Paula Sanchez Lopez, Helle K. Iversen, Sadasivan Puthusserypady

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of 2019 IEEE Region10 Conference
Publication date2019
ISBN (Electronic)978-1-7281-1895-6
Publication statusPublished - 2019
Event2019 IEEE Region10 Conference - Hotel Grand Hyatt Kochi, Kerala, India
Duration: 17 Oct 201920 Oct 2019


Conference2019 IEEE Region10 Conference
LocationHotel Grand Hyatt Kochi
Internet address


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