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

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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.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (61502340, 61172185), Natural Science Foundation of Tianjin City (15JCYBJC51800), and Higher School Science and Technology Development Fund Planning Project of Tianjin City (20120829).

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Authors and Affiliations

  1. Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, 300384, China

    Enzeng Dong, Changhai Li, Liting Li & Chao Chen

  2. Department of Mechanical Engineering, Tshwane University of Technology, Pretoria, 0001, South Africa

    Shengzhi Du

  3. Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Suita, 565-0871, Japan

    Abdelkader Nasreddine Belkacem

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  1. Enzeng Dong
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  2. Changhai Li
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  3. Liting Li
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  6. Chao Chen
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Correspondence to Chao Chen.

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Dong, E., Li, C., Li, L. et al. Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain–computer interfaces. Med Biol Eng Comput 55, 1809–1818 (2017). https://doi.org/10.1007/s11517-017-1611-4

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  • DOI: https://doi.org/10.1007/s11517-017-1611-4

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