• Please send your full manuscript to: jcm@etpub.com

Build Control Command Set Based on EEG Signals via Clustering Algorithm and Multi-Layer Neural Network

Quang Chuyen Lam1, Luong Anh Tuan Nguyen 2, Huu Khuong Nguyen 2
1. Ho Chi Minh City Industry and Trade College, Vietnam
2. Ho Chi Minh City University of Transport, Viet Nam
Abstract—Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. EEG-based control is increasingly being discovered by many researchers aims to support disabled people. In order to build control command set, the system will classify the EEG signal received of user looking at the different types of images. The good results of EEG signal classification will help make control more effectively. In this paper, a novel approach proposes the classification of EEG signals based on Wavelet transform, K-means clustering algorithm and Multi-Layer Neural Network. The system architecture was designed and evaluated with the dataset of 21,000 samples. The best accuracy rate can obtain 93.57 %.

Index Terms—EEG signals, wavelet transform, K-means, neural network


Cite: Quang Chuyen Lam, Luong Anh Tuan Nguyen, and Huu Khuong Nguyen " Build Control Command Set Based on EEG Signals via Clustering Algorithm and Multi-Layer Neural Network," Journal of Communications, vol. 13, no. 7, pp. 406-411, 2018. Doi: 10.12720/jcm.13.7.406-411.
Copyright © 2013-2017 Journal of Communications, All Rights Reserved
E-mail: jcm@etpub.com