title: Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain-computer interfaces.
abstract: Pattern classification algorithm is the crucial step in developing brain-computer interface (BCI) applications. In this paper, a hierarchical support vector machine (HSVM) algorithm is proposed to address an EEG-based four-class motor imagery classification task. Wavelet packet transform is employed to decompose raw EEG signals. Thereafter, EEG signals with effective frequency sub-bands are grouped and reconstructed. EEG feature vectors are extracted from the reconstructed EEG signals with one versus the rest common spatial patterns (OVR-CSP) and one versus one common spatial patterns (OVO-CSP). Then, a two-layer HSVM algorithm is designed for the classification of these EEG feature vectors, where "OVO" classifiers are used in the first layer and "OVR" in the second layer. A public dataset (BCI Competition IV-II-a)is employed to validate the proposed method. Fivefold cross-validation results demonstrate that the average accuracy of classification in the first layer and the second layer is 67.5 ± 17.7% and 60.3 ± 14.7%, respectively. The average accuracy of the classification is 64.4 ± 16.7% overall. These results show that the proposed method is effective for four-class motor imagery classification.
authors.affiliation: Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China.
authors.forename: Enzeng
authors.initials: E
authors.lastname: Dong
authors.affiliation: Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China.
authors.forename: Changhai
authors.initials: C
authors.lastname: Li
authors.affiliation: Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China.
authors.forename: Liting
authors.initials: L
authors.lastname: Li
authors.affiliation: Department of Mechanical Engineering, Tshwane University of Technology, Pretoria, 0001, South Africa.
authors.forename: Shengzhi
authors.initials: S
authors.lastname: Du
authors.affiliation: Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, 565-0871, Japan.
authors.forename: Abdelkader Nasreddine
authors.initials: AN
authors.lastname: Belkacem
authors.affiliation: Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China. cccovb@hotmail.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D019018:Imagery, Psychotherapy / Q000379:methods; D007092:Imagination / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D060388:Support Vector Machine; D058067:Wavelet Analysis
keywords: Common spatial pattern; Electroencephalography (EEG); Hierarchical support vector machine (HSVM); Motor imagery