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
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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
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