@inproceedings{6e6440404b33482fa38dad82ab10c545,
title = "Research on EEG based on SVM and EMD",
abstract = "To provide accurate judgment of epilepsy for doctors, this paper made a study of two methods to classify epileptic electroencephalogram (EEG) according to the difference of the waveform and energy characteristics between EEG and normal EEG. One was adopting Support Vector Machines (SVM), the other was using the combination of the Empirical Mode Decomposition (EMD) and SVM, and the accuracy rate of epilepsy EEG and the static epilepsy EEG was compared. The experimental results indicate that the second method can achieve better effect on the classification of EEG, and distinguish effectively epileptic EEG and normal EEG. The innovation of this study is that it exerts the method of EEG based on SVM and EMD effectively.",
keywords = "Classification, EEG, EMD, SVM",
author = "Xinxin Wang and Jianlin Zhao",
year = "2012",
doi = "10.1007/978-3-642-34041-3_103",
language = "英语",
isbn = "9783642340406",
series = "Communications in Computer and Information Science",
number = "PART 2",
pages = "745--751",
booktitle = "Information Computing and Applications - Third International Conference, ICICA 2012, Proceedings",
edition = "PART 2",
note = "3rd International Conference on Information Computing and Applications, ICICA 2012 ; Conference date: 14-09-2012 Through 16-09-2012",
}