A new method of EEG classification with feature extraction based on wavelet packet decomposition

被引：0

作者：

Wang, Deng ^{[1
,2
]}

Miao, Duo-Qian ^{[1
,2
]}

Wang, Rui-Zhi ^{[1
,2
]}

机构：

[1] Department of Computer Science and Technology, Tongji University

[2] Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2013年 / 41卷 / 01期

关键词：

Brain-computer interface; Feature extraction; Nonstationary EEG signal; Wavelet packet decomposition;

D O I：

10.3969/j.issn.0372-2112.2013.01.033

中图分类号：

学科分类号：

摘要：

In order to improve accuracy of mental task classification, we propose a new method of EEG classification with feature extraction. First, the raw signals are decomposed by wavelet packet decomposition (WPD). Then, using wavelet packet entropy reflecting the distribution of signal energy in time and frequency domains, the best basis of wavelet packets is selected from a wavelet packet library according to the wavelet packet entropy. Afterwards the statistical features are used to represent the best basis wavelet coefficients. Moreover, the eigenvector is obtained by calculating the asymmetry ratio of the hemispheric brainwave at each electrode in different mental tasks. Finally, the performance of the eigenvector is evaluated via a support vector machines classifier. A publicly available EEG database was used to validate this study. Compared to the conventional WPD, wavelet packet best basis decomposition and existing autoregressive feature extraction methods, the average accuracy for the proposed method ranged from 95.41% to 99.65% for ten different combinations of five mental tasks.

引用

页码：193 / 198

页数：5

共 21 条

[1] Yang L.C., Li B.M., Li G.L., Jia L., A review of brain-computer interface technology, Acta Electronica Sinica, 33, 7, pp. 1234-1241, (2005)
[2] Xu B.G., Song A.G., Fei S.M., Feature extraction and classification of EEG in online brain-computer interface, Acta Electronica Sinica, 38, 5, pp. 1025-1030, (2011)
[3] Anderson C.W., Stolz E.A., Shamsunder S., Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks, IEEE Trans Biomed Eng, 45, 3, pp. 277-286, (1998)
[4] Yang B.H., Yan G.Z., Yan R.G., The feature extraction in brain-computer interface based on best basis of wavelet packet, J Shanghai Jiaotong Univ, 39, 11, pp. 1879-1882, (2005)
[5] Li M.A., Wang R., Hao D.M., Feature extraction and classification of EEG for imagery left-right hands movement, Chin J Biomed Eng, 28, 2, pp. 166-170, (2009)
[6] Yildiz A., Akin M., Poyraz M., Kirbas G., Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction, Expert Syst Appl, 36, 4, pp. 7390-7399, (2009)
[7] Yang B.H., Yan G.Z., Yan R.G., Wu T., Feature extraction for EEG-based brain-computer interfaces by wavelet packet best basis decomposition, J Neural Eng, 3, pp. 251-256, (2006)
[8] Xue J.Z., Zhang H., Zheng C.X., Yan X.G., Wavelet packet transform for feature extraction of EEG during mental tasks, Proceedings of the 2nd International Conference on Machine Learning and Cybernetics, pp. 360-363, (2003)
[9] Wang D., Miao D.Q., Xie C., Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection, Expert Syst Appl, 38, 11, pp. 14314-14320, (2011)
[10] Wu T., Yang G.Z., Yang B.H., Sun H., EEG feature extraction based on wavelet packet decomposition for brain computer interface, Measurement, 41, 6, pp. 618-625, (2008)

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