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).
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Acknowledgments
This research is supported by the National High-Tech R and D Program of China (863 Program) under Grant 2012AA011601, National Natural Science Foundation of China under Grant 91120305, University High Level Talent Program of Guangdong, China under Grant N9120140A, Foundation for Distinguished Young Talents in Higher Education of Guangdong, China under Grant LYM11122, Project supported by Jiangmen R and D Program 2012(156) and Science Foundation for Young Teachers of Wuyi University (NO:2013zk08).
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Wang, H., Li, Y., Long, J. et al. An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface. Cogn Neurodyn 8, 399–409 (2014). https://doi.org/10.1007/s11571-014-9296-y
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DOI: https://doi.org/10.1007/s11571-014-9296-y