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BioMed Research International
Volume 2016 (2016), Article ID 9359868, 9 pages
http://dx.doi.org/10.1155/2016/9359868
Research Article

Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks

1Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey
2Applied Artificial Intelligence Research Centre, Robotics Research Lab, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey

Received 5 March 2016; Revised 30 July 2016; Accepted 21 August 2016

Academic Editor: Juan M. Corchado

Copyright © 2016 Rahib H. Abiyev et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed system is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing the control of the wheelchair. The number of experimental measurements of brain activity has been done using human control commands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of classification system based on fuzzy neural networks (FNN) is considered. The design of FNN based algorithm is used for brain-actuated control. The training data is used to design the system and then test data is applied to measure the performance of the control system. The control of the wheelchair is performed under real conditions using direction and speed control commands of the wheelchair. The approach used in the paper allows reducing the probability of misclassification and improving the control accuracy of the wheelchair.