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

Research output: Contribution to journalArticle

View graph of relations Save citation

Open

Authors

Research units

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.

Documents

Details

Original languageEnglish
Article number011001
Number of pages28
JournalJournal of Neural Engineering
Volume14
Issue number1
DOIs
Publication statusPublished - 9 Jan 2017

Bibliographic note

© IOP

    Keywords

  • support vector machines, human-computer interaction, EEG, EMG, brain-computer interface

Download statistics

No data available

Employable Graduates; Exploitable Research

Copy the text from this field...