Quitadamo L R, Cavrini F, Sbernini L, Riillo F, Bianchi L, Seri S, Saggio G
Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy. School of Life and Health Sciences, Aston Brain Center, Aston University, Birmingham, UK.
J Neural Eng. 2017 Feb;14(1):011001. doi: 10.1088/1741-2552/14/1/011001. Epub 2017 Jan 9.
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.
支持向量机（SVM）是用于检测人机交互（HCI）中生理模式的广泛应用的分类器。它们的成功归功于其通用性、稳健性以及大量免费专用工具箱的可用性。在文献中，经常报道关于SVM实现和/或参数选择的细节不足，这使得无法重现研究分析和结果。为了进行优化分类并对结果进行恰当描述，有必要对SVM的应用进行全面的批判性综述。本文的目的是通过关注脑电图（EEG）和肌电图（EMG）技术，对SVM在确定HCI的脑和肌肉模式中的应用进行综述。特别是，概述了SVM理论的基本原理，并描述了一些相关的文献实现。此外，将综述论文的详细信息列在表格中，并给出文献中SVM使用情况的统计数据。讨论了SVM对HCI的适用性，并报告了与其他分类器的批判性比较。
