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...
| Published in: | Medical & Biological Engineering & Computing Vol. 55; no. 10; pp. 1809 - 1819 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Oct2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=125206518&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125206518 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2017 vid: 55 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 125206518 125206518 144178596 NLM28238175 10.1007/s11517-017-1611-4 NLM28238175 125206518 ppf: 1809 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain-computer interfaces. aug: au: Dong, Enzeng Li, Changhai Li, Liting Du, Shengzhi Belkacem, Abdelkader Chen, Chao Belkacem, Abdelkader Nasreddine affil: Key Laboratory of Complex System Control Theory and Application , Tianjin University of Technology , Tianjin 300384 China sug: subj: Imagination Frontal Lobe Physiology Signal Processing, Computer Assisted Guided Imagery Methods Electroencephalography Methods Brain-Computer Interfaces Algorithms ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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