Spectral feature extraction of EEG signals and pattern recognition during mental tasks of 2-D cursor movements for BCI using SVM and ANN

被引:0
作者
M. Serdar Bascil
Ahmet Y. Tesneli
Feyzullah Temurtas
机构
[1] Sakarya University,Department of Electrical and Electronics Engineering
[2] Bozok University,Department of Electrical and Electronics Engineering
来源
Australasian Physical & Engineering Sciences in Medicine | 2016年 / 39卷
关键词
Brain computer interface (BCI); EEG; PSD; PCA; ICA; SVM; LS-SVM; LVQ; MLNN; PNN; k-fold cross validation;
D O I
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中图分类号
学科分类号
摘要
Brain computer interface (BCI) is a new communication way between man and machine. It identifies mental task patterns stored in electroencephalogram (EEG). So, it extracts brain electrical activities recorded by EEG and transforms them machine control commands. The main goal of BCI is to make available assistive environmental devices for paralyzed people such as computers and makes their life easier. This study deals with feature extraction and mental task pattern recognition on 2-D cursor control from EEG as offline analysis approach. The hemispherical power density changes are computed and compared on alpha–beta frequency bands with only mental imagination of cursor movements. First of all, power spectral density (PSD) features of EEG signals are extracted and high dimensional data reduced by principle component analysis (PCA) and independent component analysis (ICA) which are statistical algorithms. In the last stage, all features are classified with two types of support vector machine (SVM) which are linear and least squares (LS-SVM) and three different artificial neural network (ANN) structures which are learning vector quantization (LVQ), multilayer neural network (MLNN) and probabilistic neural network (PNN) and mental task patterns are successfully identified via k-fold cross validation technique.
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页码:665 / 676
页数:11
相关论文
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