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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Vydané v:Medical & biological engineering & computing Ročník 55; číslo 10; s. 1809 - 1818
Hlavní autori: Dong, Enzeng, Li, Changhai, Li, Liting, Du, Shengzhi, Belkacem, Abdelkader Nasreddine, Chen, Chao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2017
Springer Nature B.V
Predmet:
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
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Shrnutí: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.
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ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-017-1611-4