Genetic Algorithm Based Feature Selection Technique for Electroencephalography Data

Abstract

High dimensionality is a well-known problem that has a huge number of highlights in the data, yet none is helpful for a particular data mining task undertaking, for example, classification and grouping. Therefore, selection of features is used frequently to reduce the data set dimensionality. Feature selection is a multi-target errand, which diminishes dataset dimensionality, decreases the running time, and furthermore enhances the expected precision. In the study, our goal is to diminish the quantity of features of electroencephalography data for eye state classification and achieve the same or even better classification accuracy with the least number of features. We propose a genetic algorithm-based feature selection technique with the KNN classifier. The accuracy is improved with the selected feature subset using the proposed technique as compared to the full feature set. Results prove that the classification precision of the proposed strategy is enhanced by 3 % on average when contrasted with the accuracy without feature selection.

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  • [1] S. I. Ali and W. Shahzad, “A Feature Subset Selection Method Based on Symmetric Uncertainty and Ant Colony Optimization,” in 2012 International Conference on Emerging Technologies, Oct. 2012, pp. 1–6. https://doi.org/10.1109/ICET.2012.6375420

  • [2] P. Asvestas, A. Korda, S. Kostopoulos, I. Karanasiou, A. Ouzounoglou, K. Sidiropoulos, and G. Matsopoulos, “Use of Genetic Algorithm for the Selection of EEG Features,” in Journal of Physics: Conference Series, vol. 633, Sep. 2015. https://doi.org/10.1088/1742-6596/633/1/012123

  • [3] J. Biesiada, W. Duch, A. Kachel, K. Maczka, and S. Palucha, “Feature Ranking Methods Based on Information Entropy With Parzen Windows”, in International Conference on Research in Electrotechnology and Applied Informatics, 2004.

  • [4] M. A. Jabbar, B. L. Deekshatulu, and P. Chandra. “Classification of Heart Disease Using K-Nearest Neighbor and Genetic Algorithm”, Procedia Technology, vol. 10, pp. 85–94, 2013. https://doi.org/10.1016/j.protcy.2013.12.340

  • [5] D. Lijuan, H. Ge, W. Ma, and J. Miao. “EEG Feature Selection Method Based on Decision Tree,” Bio-Medical Materials and Engineering, vol. 26, iss. S1, pp. S1019–S1025, Aug. 2015. https://doi.org/10.3233/BME-151397

  • [6] P. A. Estévez, C. M. Held, C. A. Holzmann, C. A. Perez, J. P. Pérez, J. Heiss, M. Garrido, and P. Peirano. “Polysomnographic Pattern Recognition for Automated Classification of Sleep-Waking States in Infants,” Medical and Biological Engineering and Computing, vol. 40, iss. 1, pp. 105–113, Jan. 2002. https://doi.org/10.1007/BF02347703

  • [7] L.-Y. Hu, M.-W. Huang, S.-W. Ke, and C.-F. Tsai, “The Distance Function Effect on k-Nearest Neighbor Classification for Medical Datasets,” SpringerPlus, vol. 5, iss. 1, Dec. 2016. https://doi.org/10.1186/s40064-016-2941-7

  • [8] J. P. Kandhasamy and S. Balamurali, “Performance Analysis of Classifier Models to Predict Diabetes Mellitus,” Procedia Computer Science, vol. 47, pp. 45–51, 2015. https://doi.org/10.1016/j.procs.2015.03.182

  • [9] S. Vachiravel, “Eye State Prediction Using EEG Signal and C4.5 Decision Tree Algorithm,” International Journal of Applied Engineering Research, vol. 10, no. 68, Jan. 2015.

  • [10] Y. Kaya and H. Pehlivan, “Feature Selection Using Genetic Algorithms for Premature Ventricular Contraction Classification,” in 2015 9th International Conference on Electrical and Electronics Engineering (ELECO), 2015, pp. 1229–1232. https://doi.org/10.1109/ELECO.2015.7394628

  • [11] D. Kim, H. Han, S. Cho, and U. Chong, “Detection of Drowsiness With Eyes Open Using EEG-Based Power Spectrum Analysis,” in 2012 7th International Forum on Strategic Technology (IFOST), 2012 pp. 1–4. https://doi.org/10.1109/IFOST.2012.6357815

  • [12] S. Li, H. Wu, D. Wan, and J. Zhu, “An Effective Feature Selection Method for Hyperspectral Image Classification Based on Genetic Algorithm and Support Vector Machine,” Knowledge-Based Systems, vol. 24, iss. 1, pp. 40–48, Feb. 2011. https://doi.org/10.1016/j.knosys.2010.07.003

  • [13] T. D. Pham and D. Tran, “Emotion Recognition Using The Emotiv EPOC Device,” in International Conference on Neural Information Processing, 2012, pp. 394–399. https://doi.org/10.1007/978-3-642-34500-5_47

  • [14] K. Polat and S. Güneş, “Classification of Epileptiform EEG Using a Hybrid System Based on Decision Tree Classifier and Fast Fourier Transform,” Applied Mathematics and Computation, vol. 187, no. 2, pp. 1017–1026, Arp. 2007. https://doi.org/10.1016/j.amc.2006.09.022

  • [15] P. P. Aghaei, T. Gulrez, O. AlZoubi, G. Gargiulo, and R. A. Calvo, “Brain-Computer Interface: Next Generation Thought Controlled Distributed Video Game Development Platform,” in 2008 IEEE Symposium On Computational Intelligence and Games, 2008, pp. 251–257.

  • [16] T. Nguyen, T. H. Nguyen, K. Q. D. Truong, and Toi Van Vo, “A Mean Threshold Algorithm for Human Eye Blinking Detection Using EEG,” in 4th International Conference on Biomedical Engineering in Vietnam, 2013, pp. 275–279. https://doi.org/10.1007/978-3-642-32183-2_69

  • [17] K. F. Man, K. S. Tang, and S. Kwong, “Genetic Algorithms: Concepts and Applications in Engineering Design,” IEEE transactions on Industrial Electronics, vol. 43, iss. 5, pp. 519–534, 1996. https://doi.org/10.1109/41.538609

  • [18] T. K. Mansoori, A. Suman, and S. K. Mishra, “Feature Selection by Genetic Algorithm and SVM Classification for Cancer Detection,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, iss. 9, pp. 357–365, Sep. 2014.

  • [19] M. Oner and G. Hu, “Analyzing One-Channel EEG Signals for Detection of Close and Open Eyes Activities,” in 2013 IIAI International Conference on Advanced Applied Informatics (IIAIAAI), Aug. 2013, pp. 318–323. https://doi.org/10.1109/iiai-aai.2013.13

  • [20] O. Roesler and D. Suendermann, “A First Step Towards Eye State Prediction Using EEG,” Proc. of the AIHLS, 2013.

  • [21] S. Khadijeh, R. Boostani, and A. Ghanizadeh, “Classification of BMD and ADHD Patients Using Their EEG Signals,” Expert Systems with Applications, vol. 38, no. 3, pp. 1956–1963, Mar. 2011. https://doi.org/10.1016/j.eswa.2010.07.128

  • [22] M. Sahu, N. K. Nagwani, S. Verma, and S. Shirke. “Impact of Ranked Ordered Feature List (ROFL) on Classification with Visual Data Mining Techniques,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Mar. 2016. https://doi.org/10.1109/ICEEOT.2016.7755289

  • [23] D. A. Singh, E. J. Leavline, R., Priyanka, and P. P. Priya, “Dimensionality Reduction Using Genetic Algorithm for Improving Accuracy in Medical Diagnosis,” International Journal of Intelligent Systems and Applications, vol. 8, no. 1, pp. 67–73, Jan. 2016. https://doi.org/10.5815/ijisa.2016.01.08

  • [24] S. N. Sivanandam and S. N. Deepa, Introduction to Genetic Algorithms. Springer Science & Business Media, 2007.

  • [25] N. Sulaiman, M. Nasir T. S. Lias, Z. H. Murat, S. A. Aris, and N. H. A. Hamid, “Novel Methods for Stress Features Identification Using EEG Signals,” International Journal of Simulation: Systems, Science and Technology, vol. 12, no. 1, pp. 27–33, Feb. 2011.

  • [26] T. Michal, “Fundamentals of EEG Measurement,” Measurement Science Review, vol. 2, no. 2, pp. 1–11, 2002.

  • [27] C. F. Tsai, W. Eberle, and C. Y. Chu, “Genetic Algorithms in Feature and Instance Selection,” Knowledge-Based Systems, vol. 39, pp. 240–247, Feb. 2013. https://doi.org/10.1016/j.knosys.2012.11.005

  • [28] W. Qiuyi and E. Fokoue, “Epileptic Seizure Recognition Data Set,” 2017. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition

  • [29] M. Yeo, X. Li, K. Shen, and E. Wilder-Smith, “Can SVM be Used for Automatic EEG Detection of Drowsiness During Car Driving?,” Safety Science, vol. 47, no. 1, pp. 115–124, Jan. 2009. https://doi.org/10.1016/j.ssci.2008.01.007

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