CLASSIFICATION OF EEG SIGNAL BY METHODS OF MACHINE LEARNING

Amina ALYAMANI

amina.alyamani@yahoo.com
Omar Al-Mukhtar University, Faculty of Engineering, Department of Computer Science, West Shiha, Dernah (Libya)

Oleh YASNIY


* Ternopil Ivan Pul’uj National Technical University, Faculty of Computer Information Systems and Software Engineering, Department of Mathematical Methods in Engineering, Ruska 56, 46001, Ternopil (Ukraine)

Abstract

Electroencephalogram (EEG) signal of two healthy subjects that was available from literature, was studied using the methods of machine learning, namely, decision trees (DT), multilayer perceptron (MLP), K-nearest neighbours (kNN), and support vector machines (SVM). Since the data were imbalanced, the appropriate balancing was performed by Kmeans clustering algorithm. The original and balanced data were classified by means of the mentioned above 4 methods. It was found, that SVM showed the best result for the both datasets in terms of accuracy. MLP and kNN produce the comparable results which are almost the same. DT accuracies are the lowest for the given dataset, with 83.82% for the original data and 61.48% for the balanced data.


Keywords:

machine learning, EEG signal, classification, data balancing, feature extraction

Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175–185.
DOI: https://doi.org/10.1080/00031305.1992.10475879   Google Scholar

Amin, H. U., Mumtaz, W., Subhani, A. R., Saad, M. N. M., & Malik, A. S. (2017). Classification of EEG Signals Based on Pattern Recognition Approach. Frontiers in Computational Neuroscience, 11(103), 1–12.
DOI: https://doi.org/10.3389/fncom.2017.00103   Google Scholar

Bryant, R. A., & Sindicich, N. (2007). Hypnosis and Thought Suppression – More Data: A Brief Communication. International Journal of Clinical and Experimental Hypnosis, 56(1), 37–46.
DOI: https://doi.org/10.1080/00207140701672995   Google Scholar

Cortes, C., & Vapnik, V. N. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
DOI: https://doi.org/10.1007/BF00994018   Google Scholar

Dvey-Aharon, Z., Fogelson, N., Peled, A, & Intrator, N. (2015). Schizophrenia Detection and Classification by Advanced Analysis of EEG Recordings Using a Single Electrode Approach. PLoS ONE, 10(4), 1–12.
DOI: https://doi.org/10.1371/journal.pone.0123033   Google Scholar

Haykin, S. (Ed.). (2009). Neural Networks and Learning Machines (3rd Edition). New Jersey, Prentice Hall.
  Google Scholar

Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.
DOI: https://doi.org/10.1126/science.1127647   Google Scholar

Lawhern, V., Hairston, W. D., McDowell, K., Westerfield, M., & Robbins, K. (2012). Detection and classification of subject-generated artifacts in EEG signals using autoregressive models. Journal of Neuroscience Methods, 208(2), 181–189.
DOI: https://doi.org/10.1016/j.jneumeth.2012.05.017   Google Scholar

Li, J., Struzik, Z., Zhang, L., & Cichocki, A. (2015). Feature learning from incomplete EEG with denoising autoencoder. Neurocomputing, 165, 23–31.
DOI: https://doi.org/10.1016/j.neucom.2014.08.092   Google Scholar

MacQueen, J. B. (1967). Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability – Volume 1: Statistics, 281–297.
  Google Scholar

Parvinnia, E., Sabeti, M., Zolghadri Jahromi, M., & Boostani, R. (2014). Classification of EEG Signals using Adaptive Weighted Distance Nearest Neighbor Algorithm. Journal of King Saud University – Computer and Information Sciences, 26(1), 1–6.
DOI: https://doi.org/10.1016/j.jksuci.2013.01.001   Google Scholar

Podgorelec, V. (2012). Analyzing EEG signals with machine learning for diagnosing Alzheimer’s disease. Elektronika i Elektrotechnika, 18(8), 61–64.
DOI: https://doi.org/10.5755/j01.eee.18.8.2627   Google Scholar

Provençal, S. C., Bond, S., Rizkallah, E., & El-Baalbaki, G. (2018). Hypnosis for burn wound care pain and anxiety: A systematic review and meta-analysis. Burns, 44(8), 1870–1881.
DOI: https://doi.org/10.1016/j.burns.2018.04.017   Google Scholar

Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
DOI: https://doi.org/10.1007/BF00116251   Google Scholar

Real, R. G. L., & Kübler, A. (2014). Auditory oddball paradigm during hypnosis. Institute of Psychology, University of Würzburg.
  Google Scholar

Sanei, S., & Chambers, J. A. (Eds.). (2007). EEG Signal processing. Great Britain, Chippenham, John Wiley & Sons.
DOI: https://doi.org/10.1002/9780470511923   Google Scholar

Satapathy, S. K., Jagadev, A. K., & Dehuri, S. (2017). Weighted majority voting based ensemble of classifiers using different machine learning techniques for classification of EEG signal to detect epileptic seizure. Informatica, 41(1), 99–110.
  Google Scholar

Sun, L., Jin, B., Yang, B., Tong, J., Liu, C., & Xiong, H. (2019). Unsupervised EEG Feature Extraction Based on Echo State Network. Information Sciences, 475, 1–17.
DOI: https://doi.org/10.1016/j.ins.2018.09.057   Google Scholar

Terhune, D. B., Cleeremans, A., Raz, A., & Lynn, S. J. (2017). Hypnosis and top-down regulation of consciousness. Neuroscience and Biobehavioral Reviews, 81(A), 59–74.
DOI: https://doi.org/10.1016/j.neubiorev.2017.02.002   Google Scholar

Thilakvathi, B., Shenbaga, Devi, S., Bhanu, K., & Malaippan, M. (2017). EEG signal complexity analysis for schizophrenia during rest and mental activity. Biomedical Research, 28(1): 1–9.
  Google Scholar

Wood, C., & Bioy, A. (2008). Hypnosis and Pain in Children. Journal of Pain and Symptom Management, 35(4), 437–446.
DOI: https://doi.org/10.1016/j.jpainsymman.2007.05.009   Google Scholar

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Published
2020-12-30

Cited by

ALYAMANI, A. ., & YASNIY, O. . (2020). CLASSIFICATION OF EEG SIGNAL BY METHODS OF MACHINE LEARNING. Applied Computer Science, 16(4), 56–63. https://doi.org/10.23743/acs-2020-29

Authors

Amina ALYAMANI 
amina.alyamani@yahoo.com
Omar Al-Mukhtar University, Faculty of Engineering, Department of Computer Science, West Shiha, Dernah Libya

Authors

Oleh YASNIY 

* Ternopil Ivan Pul’uj National Technical University, Faculty of Computer Information Systems and Software Engineering, Department of Mathematical Methods in Engineering, Ruska 56, 46001, Ternopil Ukraine

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