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Classification of EEG Signal using Correlation Coefficient among Channels as Features Extraction Method


  • Department of Information Technology, Krishna Institute of Engineering and Technology, Ghaziabad - 201206, Uttar Pradesh, India
  • Department of Computer Science, Bundelkhand Institute of Engineering and Technology, Jhansi - 284128, Uttar Pradesh, India
  • Department of Electrical and Electronics, Krishna Institute of engineering and Technology, Ghaziabad - 201206, Uttar Pradesh, India


Objectives: In this paper, we have evaluated the effectiveness of classification of Electroencephalogram (EEG) signals using the correlation between channels as a method of features selection. Methods/Statistical Analysis: First data is broken sample wise, then correlation coefficient between channel pair for each sample is calculated. After that mean of the correlation coefficient of all channel pair for each class over all samples is calculated and in a similar manner, standard deviation from the mean is also calculated. For feature selection we have plotted a pair of the Gaussian curves between channels of two separate classes and choose those channels which give us lower misclassified area as features. Then these features are used for training purpose of Support Vector Machine (SVM). Findings: Most of the previous researches follow either signal processing approach or machine learning approach while we emphasized upon the nature of the signal propagation amongst the neurons. The basic idea behind the feature selection is taken from the way the signals propagate from one neuron to the other. In our work we assume that EEG signals follow the normal distribution and verify the fact using chi-square test. On applying SVM the accuracy of classification on testing data confirms that correlation among channels can be used for feature selection. Application/Improvements: The results can be improved by improving the pre-processing of EEG signals. It can be used to develop a Brain Computer Interaction (BCI) system.


Correlation Coefficient, Electroencephalogram (EEG), Support Vector Machine (SVM).

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  • Alomari MH, AbuBaker A, Turani A, Baniyounes AM, Manasreh A. EEG mouse: A machine learning-based brain computer interface. International Journal of Advanced Research in Computer Science and Applications. 2014; 5(4):1–6.
  • Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain–computer interfaces. Journal of Neural Engineering. 2007 Jun; 4(2):1–13.
  • Soman S. High performance EEG signal classification using classifiability and the twin SVM. Applied Soft Computing. 2015 May; 30:305–18.
  • Xiao D, Mu Z, Hu J. Classification of motor imagery eegsignals based on energy entropy. IEEE Proceedings of the International Symposium on Intelligent Ubiquitous Computing and Education; 2009 May. p. 61–4.
  • Ke L, Li R. Classification of EEG signals by multi-scale filtering and PCA. Proceedings of IEEE International Conference on Intelligent Computing and Intelligent Systems; 2009 Nov. p. 362–6.
  • Oveisi F. EEG signal classification using nonlinear independent component analysis. Proceedings of ICASSP IEEE International Conference on Acoustics, Speech and Signal; 2009 Apr. p. 361–4.
  • Skinner BT, Nguyen HT, Liu DK. Classification of EEG signals using a genetic-based machine learning classifier. Proceedings of IEEE Conference of Engineering Medicine Biology Society. 2007 Aug. p. 3120–3.
  • Anderson C, Stolz E, Shamsunder S. Multivariate autoregressive models for classification of spontaneous electroencephalogram during mental tasks. IEEE Transactions on Biomedical Engineering. 1998 Mar; 45(3):277–86.
  • Vatankhah M, Yaghubi M. Adaptive neuro-fuzzy inference system for classification of EEG signals using fractal dimension. 3rd UKSim European Symposium on Computer Modeling and Simulation; 2009 Nov. p. 214–8.
  • Yazdani A, Ebrahimi T, Hoffmann U. Classification of EEG signals using dempster shafer theory and a K-nearest neighbor classifier. Proceedings of the 4th International IEEE EMBS Conference on Neural Engineering Antalya; Turkey. 2009 Apr-May. p. 327–30.
  • Zhovna I, Shallom ID. Automatic detection and classification of sleep stages by multichannel EEG signal modeling. Proceedings of 30th Annual International IEEE EMBS Conference; Vancouver, British Columbia, Canada. 2008Aug. p. 2665–8.
  • Blankertz B, Muller K, Krusienski D, Schalk G, Wolpaw J, Schlogl A. The BCI competition III: Validating alternative approaches to actual BCI problems. IEEE Transaction on Neural System Rehability Energy. 2006 Jun; 14(2):153–9.


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