Support vector EEG classification in the Fourier and time-frequency correlation domains

被引：42

作者：

Garcia, GN ^{[1
]}

Ebrahimi, T ^{[1
]}

Vesin, JM ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, EPFL, CH-1015 Lausanne, Switzerland

来源：

1ST INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2003, CONFERENCE PROCEEDINGS | 2003年

关键词：

direct brain-computer communication; EEG classification; SVM; optimal SVM parameter choice; time-frequency correlation;

D O I：

10.1109/CNE.2003.1196897

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we use support vector machines (SVM) for classifying EEG signals corresponding to imagined motor movements. The parameters of an SVM Kernel are optimized for minimizing a theoretical error bound. Fourier features and correlative time-frequency based features are extracted from EEG signals and compared with respect to their discriminatory power.

引用

页码：591 / 594

页数：4

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