Open Access Open Access  Restricted Access Subscription or Fee Access

Combined Approaches of Features Selection for EEG Classification

(*) Corresponding author

Authors' affiliations



The classification accuracy of EEG signal illustrates the degree of recognition of significant characteristics describing events revealing underlying neuronal activity. In this paper we focus on features selection, by using both t-test and entropy criteria. The feature extraction is accomplished using wavelet functions Symmlet5, Coiflet5, and DB10, and classification is achieved using Linear Discriminate Analysis and Support Vector Machines classifiers. The obtained results show an improvement in classification accuracy reaching 7% for LDA classifier, and 4% for SVM classifier.
Copyright © 2015 Praise Worthy Prize - All rights reserved.


Classification; EEG; Entropy; Features selection; T-Test

Full Text:



A. Bashashati, M. Fatourechi, R. K. Ward, G. E. Birch, A Survey of Signal Processing Algorithms in Brain-computer Interfaces Based on Electrical Brain Signals, Journal of Neural Engineering, Vol. 4, pp. 32-57, 2007.

A. Subasi, EEG Signal Classification using Wavelet Features Extraction and a mixture of Expert Model, Expert Systems with Applications, Vol. 32, pp. 1084-1093, 2007.

A. C. Haury, P. Gestraud, J. P. Vert, The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures. PLoS ONE, Vol. 6, n. 12, e28210, 2011.

J. Novakovic, P. Strbac, D. Bulatovic, Toward Optimal feature selection using Ranking Methods And Classification Algorithms, Yugoslav Journal of Operations Research, Vol.21, n.1, pp. 119-135, 2011.

J. R. Vergara, P. A. Estévez, A review of Feature Selection Methods Based on Mutual Information, Neural Computing and Applications, Vol. 24, n. 1, pp. 175-186, 2014.

N. Poolsawad, C. Kambhampati, and J. G. F. Cleland, Feature Selection Approaches with Missing Values Handling for Data Mining - A case Study of Heart Failure Dataset, World Academy of Science, Engineering and Technology, Vol. 5, pp. 12-20, 2011.

I. Homri, S. Yacoub, N. Ellouze, EEG classification for motor imagery, IJACT, Vol. 5, n. 9, pp. 264-273, 2013.

G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, EEG based discrimination between imagination of right and left hand movement, Electroenceph clin Neurophysiol, Vol. 103, pp. 642-651, 1997.

G. Pfurtscheller, A. Stancfik Jr., G. Edlinger, On the existence of different types of central beta rhythms below 30 Hz, Electroenceph clin Neurophysiol, Vol.102, pp. 316-325, 1997.

Elouaham, S., Latif, R., Nassiri, B., Dliou, A., Laaboubi, M., Maoulainine, F., Analysis electroencephalogram signals using ANFIS and periodogram techniques, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2959-2966.

S. Theodoridis, and K. Koutroumbas, Pattern Recognition, (Academic Press, pp. 341-342, 1999).

S. Wang, H. Chen, S. Li, and D. Zhang, Feature Extraction from Tumor Gene Expression Profiles Using DCT and DFT, J. Neves, M. Santos, and J. Machado (Eds.): EPIA 2007, LNAI, Vol. 4874, pp. 485–496, Springer-Verlag Berlin Heidelberg 2007.

A.K. Jain, R.P.W. Duin, J. Mao, A Review, Statistical Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, n. 1, pp. 4-37, 2000.

I. Rejer, K. Lorenz , Genetic algorithm and forward method for feature selection in EEG feature space, Journal of Theoretical and Applied Computer Science, Vol. 7, n. 2, pp. 72-82, 2013.

F. Lotte, M. Congedo, A. Lecuyer, F. Lamarche, A review of classication algorithms for EEG-based brain computer interfaces. Journal of Neural Engineering, Institute of Physics: Hybrid Open Access, Vol. 4, < inria-00134950>, 2007.

V. Vapnik, Statistical Learning Theory (New York: Wiley), 1998

Vallabhaneni, R.B., Rajesh, V., Brain tumor segmentation with wavelet watershed and detection using MULTI-SVM classifier, (2014) International Review on Computers and Software (IRECOS), 9 (11), pp. 1807-1815.

C-C. Chang, C-J. Lin, LIB-SVM: a library for support vector machines, 2001. Software available at

Khazaee, A., Ebrahimzadeh, A., Electrocardiogram beat classification using support vector machines and efficient features, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (6), pp. 515-521.

Kareim, A.A., Mansor, M.B., Support vector machine for MPPT efficiency improvement in photovoltaic system, (2013) International Review of Automatic Control (IREACO), 6 (2), pp. 177-182.

Arikan, C., Ozdemir, M., Classification of power quality disturbances using support vector machines and comparing classification performance, (2013) International Review of Electrical Engineering (IREE), 8 (2), pp. 776-784.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize