CLASSIFICATION OF EEG SIGNAL BY METHODS OF MACHINE LEARNING
Amina ALYAMANI
amina.alyamani@yahoo.comOmar 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 extractionReferences
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
Authors
Amina ALYAMANIamina.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
Statistics
Abstract views: 244PDF downloads: 30
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
Similar Articles
- Robert KARPIŃSKI, Józef JONAK, Jacek MAKSYMIUK, MEDICAL IMAGING AND 3D RECONSTRUCTION FOR OBTAINING THE GEOMETRICAL AND PHYSICAL MODEL OF A CONGENITAL BILATERAL RADIO-ULNAR SYNOSTOSIS , Applied Computer Science: Vol. 14 No. 1 (2018)
- Jarelh Galdos, Nikolai Lopez, Angie Medina, Jorge Huarca, Jorge Rendulich, Erasmo Sulla, COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES , Applied Computer Science: Vol. 20 No. 1 (2024)
- Ziadeddine MAKHLOUF, Abdallah MERAOUMIA , Laimeche LAKHDAR, Mohamed Yassine HAOUAM , ENHANCING MEDICAL DATA SECURITY IN E-HEALTH SYSTEMS USING BIOMETRIC-BASED WATERMARKING , Applied Computer Science: Vol. 20 No. 1 (2024)
- Alexis J. LOPEZ, Perfecto M. QUINTERO, Ana K. HERNANDEZ, ANALYTICS AND DATA SCIENCE APPLIED TO THE TRAJECTORY OUTLIER DETECTION , Applied Computer Science: Vol. 16 No. 2 (2020)
- Wojciech DANILCZUK, Arkadiusz GOLA, COMPUTER-AIDED MATERIAL DEMAND PLANNING USING ERP SYSTEMS AND BUSINESS INTELLIGENCE TECHNOLOGY , Applied Computer Science: Vol. 16 No. 3 (2020)
- Nancy WOODS, Gideon BABATUNDE, A ROBUST ENSEMBLE MODEL FOR SPOKEN LANGUAGE RECOGNITION , Applied Computer Science: Vol. 16 No. 3 (2020)
- Ihor PYSMENNYI, Anatolii PETRENKO, Roman KYSLYI, GRAPH-BASED FOG COMPUTING NETWORK MODEL , Applied Computer Science: Vol. 16 No. 4 (2020)
- Saheed ADEWUYI, Segun AINA, Aderonke LAWAL, Adeniran OLUWARANTI, Moses UZUNUIGBE, AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING , Applied Computer Science: Vol. 15 No. 4 (2019)
- Nouhaila BOUALOULOU, Taoufiq BELHOUSSINE DRISSI, Benayad NSIRI, CNN AND LSTM FOR THE CLASSIFICATION OF PARKINSON'S DISEASE BASED ON THE GTCC AND MFCC , Applied Computer Science: Vol. 19 No. 2 (2023)
- Katarzyna GOSPODAREK, DETERMINATION OF RELATIVE LENGTHS OF BONE SEGMENTS OF THE DOMESTIC CAT'S LIMBS BASED ON THE DIGITAL IMAGE ANALYSIS , Applied Computer Science: Vol. 15 No. 2 (2019)
<< < 3 4 5 6 7 8 9 10 11 12 > >>
You may also start an advanced similarity search for this article.
