TITLE:
Mean Threshold and ARNN Algorithms for Identification of Eye Commands in an EEG-Controlled Wheelchair
AUTHORS:
Nguyen Thanh Hai, Nguyen Van Trung, Vo Van Toi
KEYWORDS:
Autoregressive NN Model; Threshold algorithm; EEG Technology; Eye Activity and Electrical Wheelchair
JOURNAL NAME:
Engineering,
Vol.5 No.10B,
October
31,
2013
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.