[EEG signal classification based on EMD and SVM].

Shufang Li、Weidong Zhou、Dongmei Cai、Kai Liu、Jianlin Zhao

2011PubMedNeuroscience被引 9

摘要

The automatic detection and classification of EEG epileptic wave have great clinical significance. This paper proposes an empirical mode decomposition (EMD) and support vector machine (SVM) based classification method for non-stationary EEG. Firstly, EMD was used to decompose EEG into multiple empirical mode components. Secondly, effective features were extracted from the scales. Finally, the EEG was classified with SVM. The experiment indicated that this method could achieve good classification result with accuracy of 99 % for interictal and ictal EEGs.

引用本文（GB/T 7714）

Shufang Li, Weidong Zhou, Dongmei Cai, 等. [EEG signal classification based on EMD and SVM].[J]. PubMed, 2011.

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