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

Autor:	Rostam, Zhyar Rzgar K., Mahmood, Sozan Abdullah
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Zdroj:	JZS (2019) 21_2 35_44
Druh dokumentu:	Working Paper
DOI:	10.17656/jzs.10755
Popis:	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. Comment: 10 pages, 6 tables, and 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.07361 Zobrazit plný text záznamu View this record from Arxiv