Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Quitadamo, L.R., Cavrini, F., Sbernini, L., Riillo, F., Bianchi, L., Seri, S. and Saggio, G. (2017). Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review. Journal of Neural Engineering, 14 (1),

Abstract

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Publication DOI: https://doi.org/10.1088/1741-2552/14/1/011001
Divisions: Life & Health Sciences
Life & Health Sciences > Psychology
Life & Health Sciences > Clinical and Systems Neuroscience
Life & Health Sciences > Aston Brain Centre
Additional Information: © IOP
Uncontrolled Keywords: support vector machines,human-computer interaction,EEG,EMG,brain-computer interface,Biomedical Engineering,Cellular and Molecular Neuroscience
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2017-01-09
Authors: Quitadamo, L.R. ( 0000-0003-1877-4672)
Cavrini, F.
Sbernini, L.
Riillo, F.
Bianchi, L.
Seri, S. ( 0000-0002-9247-8102)
Saggio, G.

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