A self-training semi-supervised SVM algorithm and its application in an EEG-based brain computer interface speller system

被引：188

作者：

Li, Yuanqing ^{[1
]}

Guan, Cuntai ^{[1
]}

Li, Huiqi ^{[1
]}

Chin, Zhengyang ^{[1
]}

机构：

[1] Inst Inforcomm Res, Singapore 119613, Singapore

来源：

PATTERN RECOGNITION LETTERS | 2008年 / 29卷 / 09期

关键词：

semi-supervised support vector machine (SVM); model selection; convergence; brain computer interface (BCI); electroencephalogram (EEG);

D O I：

10.1016/j.patrec.2008.01.030

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we first present a self-training semi-supervised support vector machine (SVM) algorithm and its corresponding model selection method, which are designed to train a classifier with small training data. Next, we prove the convergence of this algorithm. Two examples are presented to demonstrate the validity of our algorithm with model selection. Finally, we apply our algorithm to a data set collected from a P300-based brain computer interface (BCI) speller. This algorithm is shown to be able to significantly reduce training effort of the P300-based BCI speller. (c) 2008 Elsevier B.V. All rights reserved.

引用

页码：1285 / 1294

页数：10

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