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Citation |
Yu Y, Zhou Z, Yin E, Jiang J, Tang J, Liu Y, Hu D. Comput. Biol. Med. 2016; 77: 148-155. |
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Affiliation |
College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, Hunan 410073, PR China. |
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Copyright |
(Copyright © 2016, Elsevier Publishing) |
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DOI |
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PMID |
27544071 |
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Abstract |
This study presented a paradigm for controlling a car using an asynchronous electroencephalogram (EEG)-based brain-computer interface (BCI) and presented the experimental results of a simulation performed in an experimental environment outside the laboratory. This paradigm uses two distinct MI tasks, imaginary left- and right-hand movements, to generate a multi-task car control strategy consisting of starting the engine, moving forward, turning left, turning right, moving backward, and stopping the engine. Five healthy subjects participated in the online car control experiment, and all successfully controlled the car by following a previously outlined route. Subject S1 exhibited the most satisfactory BCI-based performance, which was comparable to the manual control-based performance. We hypothesize that the proposed self-paced car control paradigm based on EEG signals could potentially be used in car control applications, and we provide a complementary or alternative way for individuals with locked-in disorders to achieve more mobility in the future, as well as providing a supplementary car-driving strategy to assist healthy people in driving a car. Language: en |