Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain–computer interfaces

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...

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Veröffentlicht in:	Medical & biological engineering & computing Jg. 55; H. 10; S. 1809 - 1818
Hauptverfasser:	Dong, Enzeng, Li, Changhai, Li, Liting, Du, Shengzhi, Belkacem, Abdelkader Nasreddine, Chen, Chao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017 Springer Nature B.V
Schlagworte:	Algorithms Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Brain Classification Competition Computer Applications EEG Electroencephalography Emulation Feature extraction Human Physiology Image classification Imaging Interfaces Mental task performance Original Article Radiology Support vector machines Wavelet analysis Motor imagery Electroencephalography (EEG) Common spatial pattern Hierarchical support vector machine (HSVM)
ISSN:	0140-0118, 1741-0444, 1741-0444
Online-Zugang:	Volltext
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