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
收藏
页码：591 / 594
页数：4
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