Use of ANNs as classifiers for selective attention brain-computer interfaces

被引：0

作者：

Angel Lopez, Miguel ^{[1
]}

Pomares, Hector ^{[1
]}

Damas, Miguel ^{[1
]}

Madrid, Eduardo ^{[2
]}

Prieto, Alberto ^{[1
]}

Pelayo, Francisco ^{[1
]}

de la Plaza Hernandez, Eva Maria ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Architecture & Comp Technol, E-18071 Granada, Spain

[2] Univ Granada, Dept expt Psychol & Physiol Behav, E-18071 Granada, Spain

来源：

COMPUTATIONAL AND AMBIENT INTELLIGENCE | 2007年 / 4507卷

关键词：

Artificial Neural Networks; brain-computer interfaces; selective attention; Auditory Steady-state Response;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based brain-computer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of ANNs as classifiers for a selective attention to auditory stimuli based BCI system.

引用

页码：956 / +

页数：3

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