Trial Pruning for Classification of Single-Trial EEG Data during Motor Imagery

被引：4

作者：

Wang, Boyu ^{[1
]}

Wong, Chiman ^{[1
]}

Wan, Feng ^{[1
]}

Mak, Peng Un ^{[1
]}

Mak, Pui In ^{[1
]}

Vai, Mang I. ^{[1
]}

机构：

[1] Univ Macau, Dept Elect & Elect Engn, Fac Sci & Technol, Taipa, Peoples R China

来源：

2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2010年

关键词：

BRAIN-COMPUTER INTERFACES; ALGORITHMS;

D O I：

10.1109/IEMBS.2010.5626453

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Due to the artifacts in electroencephalography (EEG) data, the performance of brain-computer interface (BCI) is degraded. On the other hand, in the motor imagery based BCI system, EEG signals are usually contaminated by the misleading trials caused by improper imagination of a movement. In this paper, we present a novel algorithm to detect the abnormal EEG data using genetic algorithm (GA). After trial pruning, a subset of the EEG data are selected, on which common spatial pattern (CSP) and Gaussian classifier are trained. The performance of the proposed method is tested on Data set IIa of BCI Competition IV, and the simulation result demonstrates a significant improvement for six out of nine subjects.

引用

页码：4666 / 4669

页数：4

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