NDLI: An autoregressive neural network for recognition of eye commands in an EEG-controlled wheelchair

Content Provider	IEEE Xplore Digital Library
Author	Hai Thanh Nguyen Nguyen Trung Vo Toi Van-Su Tran
Copyright Year	2013
Description	Author affiliation: Biomed. Eng. Dept., Int. Univ., Ho Chi Minh City, Vietnam (Hai Thanh Nguyen; Vo Toi) \|\| Sch. of Electr. Eng., Int. Univ., Ho Chi Minh City, Vietnam (Van-Su Tran) \|\| Fac. of Electr.-Electron. Eng., UTE-HCMC, Ho Chi Minh City, Vietnam (Nguyen Trung)
Abstract	This paper represents an autoregressive (AR) neural network for recognizing eye movement commands for control of an electrical wheelchair using EEG technology. The eye movements such as opening eyes, blinking eyes, 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 movement 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 (NN) model to classify the eye activities. From the identified output of the network, the wheelchair was controlled to follow the desired direction of user. Experimental results of controlling the wheelchair in the indoor environment showed to illustrate the effectiveness of the proposed approach.
Starting Page	333
Ending Page	338
File Size	643291
Page Count	6
File Format	PDF
ISBN	9781479910861
ISSN	21621020
e-ISBN	9781479910892
DOI	10.1109/ATC.2013.6698132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-16
Publisher Place	Vietnam
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Band-pass filters Wheelchairs Brain modeling Feature extraction Electroencephalography Vectors Autoregressive model Neural network Eye activity and Electrical wheelchair Biological neural networks EEG technology
Content Type	Text
Resource Type	Article

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