This book introduces two Autoregressive Neural Network (ARNN) and mean threshold methods for recognizing eye commands for control of an electrical wheelchair using Electroencephalogram (EEG) technology. Eye movements such as eyes open, eyes blink, glancing left and glancing right. A Hamming low pass filter was applied to remove artifacts of eye signals for extracting the frequency ranges. An AR 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 applied for classifying eye movements. In comparison of two recognition methods, the purpose was to find the better one for applying in the electrical wheelchair.

Nguyen Thanh Hai obtained his Ph.D in Electronics-Engineering at University of Technology, Sydney, Australia, in 2010. Currently he is a lecturer in Faculty of Electrical-Electronics Engineering, Ho Chi Minh City University of Technical Education, Vietnam. Research interests are development of smart wheelchair, artificial intelligent and Bio-signal