Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey.
Applied Artificial Intelligence Research Centre, Robotics Research Lab, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey.
Biomed Res Int. 2016;2016:9359868. doi: 10.1155/2016/9359868. Epub 2016 Sep 29.
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.
为身体残疾人士设计了一种脑-机接口轮椅。该系统的设计基于接收、处理和分类脑电图（EEG）信号，然后对轮椅进行控制。使用人类控制轮椅的命令进行了脑活动的实验测量次数。基于用户的精神活动和轮椅的控制命令，考虑了基于模糊神经网络（FNN）的分类系统的设计。基于 FNN 的算法设计用于脑驱动控制。使用系统设计的训练数据，然后应用测试数据来测量控制系统的性能。使用轮椅的方向和速度控制命令在实际条件下进行轮椅控制。本文采用的方法可以降低分类错误的概率，提高轮椅控制的准确性。
