Classification of motor imagery EEG signals based on STFTs

被引：0

作者：

Mu, Zhendong ^{[1
]}

Xiao, Dan ^{[1
]}

Hu, Jianfeng ^{[1
]}

机构：

[1] Jiangxi Bluesky Univ, Inst Informat & Technol, Nanchang 330098, Jiangxi, Peoples R China

来源：

PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9 | 2009年

关键词：

Brain computer interface (BCI); time-frequency analysis; Fisher distance; EEG(electroencephalogram); COMPUTER INTERFACE BCI; NEURAL-NETWORKS; WAVELET TRANSFORM; BRAIN POTENTIALS; TIME; RECOGNITION;

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：

Human motor imagery tasks evoke electroencephalogram (EEG) signal changes. We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings. The technique is based on a time-frequency analysis of EEG signals, regarding the relations between the EEG data obtained from the C3/C4 electrodes, the features were reduced according the Fisher distance. This reduced feature set is finally fed to a linear discriminant for classification. The algorithm was applied to 3 subjects, the classification performance of the proposed algorithm varied between 70% and 93.1%; across subjects.

引用

页码：181 / 184

页数：4

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