Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair

Nguyen Thanh Hai, Nguyen Van Trung, Vo Van Toi

Biomedical Engineering Department, International University, Vietnam National University, Ho Chi Minh City, Vietnam.
Faculty of Electrical-Electronics Engineering, University of Technical Education, Ho Chi Minh City, Vietnam.

DOI: 10.4236/eng.2013.510B059                

Abstract

This paper represented Autoregressive Neural Network (ARNN) and meant threshold methods for recognizing eye movements for control of an electrical wheelchair using EEG technology. The eye movements such as eyes open, eyes blinks, glancing left and glancing right related to a few areas of human brain were investigated. A Hamming low pass filter was applied to remove noise and artifacts of the eye signals and to extract the frequency range of the measured signals. An autoregressive model was employed to produce coefficients containing features of the EEG eye signals. The coefficients obtained were inserted the input layer of a neural network model to classify the eye activities. In addition, a mean threshold algorithm was employed for classifying eye movements. Two methods were compared to find the better one for applying in the wheelchair control to follow users to reach the desired direction. Experimental results of controlling the wheelchair in the indoor environment illustrated the effectiveness of the proposed approaches.

Keywords

Autoregressive NN Model; Threshold algorithm; EEG Technology; Eye Activity and Electrical Wheelchair

Conflicts of Interest

The authors declare no conflicts of interest.

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