TY - JOUR
T1 - Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain–computer interfaces
AU - Dong, Enzeng
AU - Li, Changhai
AU - Li, Liting
AU - Du, Shengzhi
AU - Belkacem, Abdelkader Nasreddine
AU - Chen, Chao
N1 - Publisher Copyright:
© 2017, International Federation for Medical and Biological Engineering.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - 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.
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.
KW - Common spatial pattern
KW - Electroencephalography (EEG)
KW - Hierarchical support vector machine (HSVM)
KW - Motor imagery
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U2 - 10.1007/s11517-017-1611-4
DO - 10.1007/s11517-017-1611-4
M3 - Article
AN - SCOPUS:85013770887
SN - 0140-0118
VL - 55
SP - 1809
EP - 1818
JO - Medical and Biological Engineering and Computing
JF - Medical and Biological Engineering and Computing
IS - 10
ER -