Multi-objective metaheuristics for preprocessing EEG data in brain-computer interfaces

被引：4

作者：

Aler, Ricardo ^{[1
]}

Vega, Alicia

Galvan, Ines M. ^{[1
]}

Nebro, Antonio J. ^{[2
]}

机构：

[1] Univ Carlos III Madrid, E-28903 Getafe, Spain

[2] Univ Malaga, E-29071 Malaga, Spain

来源：

ENGINEERING OPTIMIZATION | 2012年 / 44卷 / 03期

关键词：

brain-computer interface; multi-objective optimization; EEG filter optimization; CLASSIFICATION; OPTIMIZATION; ALGORITHMS; EVOLUTION; FILTERS;

D O I：

10.1080/0305215X.2011.641542

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In the field of brain-computer interfaces, one of the main issues is to classify the electroencephalogram (EEG) accurately. EEG signals have a good temporal resolution, but a low spatial one. In this article, metaheuristics are used to compute spatial filters to improve the spatial resolution. Additionally, from a physiological point of view, not all frequency bands are equally relevant. Both spatial filters and relevant frequency bands are user-dependent. In this article a multi-objective formulation for spatial filter optimization and frequency-band selection is proposed. Several multi-objective metaheuristics have been tested for this purpose. The experimental results show, in general, that multi-objective algorithms are able to select a subset of the available frequency bands, while maintaining or improving the accuracy obtained with the whole set. Also, among the different metaheuristics tested, GDE3, which is based on differential evolution, is the most useful algorithm in this context.

引用

页码：373 / 390

页数：18

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