Classification of Brainwave Signals Based on Hybrid Deep Learning and an Evolutionary Algorithm

Zhyar Rzgar K. Rostam1, Sozan Abdullah Mahmood1

1 Department of Computer, College of Science, University of Sulaimani, Sulaimani, Kurdistan Region, Iraq.

Original: 24 April 2019, Revised: 15 June 2019, Accepted: 22 July 2019, Published online: 20 December 2019

DOI Link: https://doi.org/10.17656/jzs.10754

Abstract

Brainwave signals are read through Electroencephalogram (EEG) devices. These signals are generated from an active brain based on brain activities and thoughts. The classification of

brainwave signals is a challenging task due to its non-stationary nature. To address the issue, this paper proposes a Convolutional Neural Network (CNN) model to classify brainwave

signals. In order to evaluate the performance of the proposed model a dataset is developed by recording brainwave signals for two conditions, which are visible and invisible. In the

visible mode, the human subjects focus on the color and shape presented. Meanwhile, in the invisible mode, the subjects think about specific colors or shapes with closed eyes. A

comparison has been provided between the original CNN and the proposed CNN architecture on the same dataset. The results show that the proposed CNN model achieves

higher classification accuracy as compared to the standard CNN. The best accuracy rate achieved when the proposed CNN is applied on the visible color mode is 92%. In the future,

improvements on the proposed CNN will be able to classify raw EEG signals in an efficient way.

Key Words: Convolutional Neural Network (CNN), Genetic Algorithm (GA), Feature Extraction (Coiflet), NeuroSky mindwave.

References

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Copyright © 2019 | The Authors, published by University of Sulaimani

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