title: An asynchronous wheelchair control by hybrid EEG-EOG brain-computer interface.
abstract: Wheelchair control requires multiple degrees of freedom and fast intention detection, which makes electroencephalography (EEG)-based wheelchair control a big challenge. In our previous study, we have achieved direction (turning left and right) and speed (acceleration and deceleration) control of a wheelchair using a hybrid brain-computer interface (BCI) combining motor imagery and P300 potentials. In this paper, we proposed hybrid EEG-EOG BCI, which combines motor imagery, P300 potentials, and eye blinking to implement forward, backward, and stop control of a wheelchair. By performing relevant activities, users (e.g., those with amyotrophic lateral sclerosis and locked-in syndrome) can navigate the wheelchair with seven steering behaviors. Experimental results on four healthy subjects not only demonstrate the efficiency and robustness of our brain-controlled wheelchair system but also indicate that all the four subjects could control the wheelchair spontaneously and efficiently without any other assistance (e.g., an automatic navigation system).
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640 China ; School of Information Engineering, Wuyi University, Jiangmen, 529020 China ; Engineering Research Center for Massive Biometric Information Processing, Jiangmen, Guangdong Province China.
authors.forename: Hongtao
authors.initials: H
authors.lastname: Wang
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640 China.
authors.forename: Yuanqing
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640 China.
authors.forename: Jinyi
authors.initials: J
authors.lastname: Long
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640 China.
authors.forename: Tianyou
authors.initials: T
authors.lastname: Yu
authors.affiliation: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640 China.
authors.forename: Zhenghui
authors.initials: Z
authors.lastname: Gu
keywords: Asynchronous; Brain-controlled wheelchair; Eye blinking; Hybrid brain–computer interface; Motor imagery; P300 potentials