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Neural Computing and Applications

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01-03-2016 | Original Article

Combined feature extraction method for classification of EEG signals

Authors: Yong Zhang, Xiaomin Ji, Bo Liu, Dan Huang, Fuding Xie, Yuting Zhang

Published in: Neural Computing and Applications | Issue 11/2017

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Abstract

Classification of electroencephalogram (EEG) signals is an important task in brain–computer interfaces applications. This paper combines autoregressive (AR) model and sample entropy and presents a combination strategy of feature extraction. Each feature vector obtained from the combination strategy contains two parts: AR coefficients and sample entropy values. In the classification phase, this paper employs support vector machine (SVM) with RBF kernel as the classifier. The proposed method is used in the five mental task experiments. Experimental results show that the SVM classifier performs very well in classifying EEG signals using the combination strategy of feature extraction. It obtains a better accuracy in comparison with AR-based method. The results also indicate that the combination strategy of AR model and sample entropy can effectively improve the classification performance of EEG signals.

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Title: Combined feature extraction method for classification of EEG signals
Authors: Yong Zhang
Xiaomin Ji
Bo Liu
Dan Huang
Fuding Xie
Yuting Zhang
Publication date: 01-03-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 11/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2230-y

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