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


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:
Divisions: College of Health & Life Sciences
College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences > Clinical and Systems Neuroscience
College of Health & Life Sciences > Aston Institute of Health & Neurodevelopment (AIHN)
Additional Information: © IOP
Uncontrolled Keywords: support vector machines,human-computer interaction,EEG,EMG,brain-computer interface,Biomedical Engineering,Cellular and Molecular Neuroscience
Publication ISSN: 1741-2560
Last Modified: 22 May 2024 07:13
Date Deposited: 21 Nov 2016 13:40
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017-01-09
Accepted Date: 2016-11-07
Submitted Date: 2015-07-23
Authors: Quitadamo, L.R. (ORCID Profile 0000-0003-1877-4672)
Cavrini, F.
Sbernini, L.
Riillo, F.
Bianchi, L.
Seri, S. (ORCID Profile 0000-0002-9247-8102)
Saggio, G.



Version: Accepted Version

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