Classifying BCI signals from novice users with extreme learning machine

被引：2

作者：

Rodriguez-Bermudez, German ^{[1
]}

Bueno-Crespo, Andres ^{[2
]}

Jose Martinez-Albaladejo, F. ^{[3
]}

机构：

[1] Spanish Air Force Acad, Univ Ctr Def, Ctr Univ Def San Javier, Murcia, Spain

[2] Univ Catolica Murcia, Bioinformat & High Performance Comp Res Grp BIO H, Murcia, Spain

[3] Univ Catolica Murcia, Telecommun Dept, Murcia, Spain

来源：

OPEN PHYSICS | 2017年 / 15卷 / 01期

关键词：

Brain computer interface; motor imagery; extreme learning machine; novice users; BRAIN-COMPUTER INTERFACE; MOTOR IMAGERY; CLASSIFICATION; EEG; ALGORITHMS;

D O I：

10.1515/phys-2017-0056

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

引用

页码：494 / 500

页数：7

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