An online semi-supervised P300 speller based on extreme learning machine

被引：14

作者：

Wang, Junjie ^{[1
]}

Gu, Zhenghui ^{[1
]}

Yu, Zhuliang ^{[1
]}

Li, Yuanqing ^{[1
]}

机构：

[1] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China

来源：

NEUROCOMPUTING | 2017年 / 269卷

基金：

中国国家自然科学基金;

关键词：

Brain-computer interface; Semi-supervised learning; Extreme learning machine; CLASSIFICATION; ALGORITHM; NETWORKS;

D O I：

10.1016/j.neucom.2016.12.098

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Semi-supervised learning has been applied in brain-computer interfaces (BCIs) to reduce calibration time for user. For example, a sequential updated self-training least squares support vector machine (SUST-LSSVM) was devised for online semi-supervised P300 speller. Despite its good performance, the computational complexity becomes too high after several updates, which hinders its practical online application. In this paper, we present a self-training regularized weighted online sequential extreme learning machine (ST-RWOS-ELM) for P300 speller. It achieves much lower complexity compared to SUST-LSSVM without affecting the spelling accuracy performance. The experimental results validate its effectiveness in the P300 system. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：148 / 151

页数：4

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