Human factors in the design of BCI-controlled wheelchairs

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Abstract

In this paper, we synthesize research on the type of cognitive commands that have been examined for controlling Brain Computer Interface (BCI) wheelchairs and the human factors that have been reported for the selection of different protocols of BCI commands for an individual user. Moreover, we investigate how different researchers have considered the necessity of sustained movement from a single thought/command, having an emergency stop, and the commands necessary for assisting users with a particular disability. We then highlight how these human factors and ergonomics' considerations were applied in the design and development of an EEG-controlled motorized wheelchair, aiming to emphasize users' requirements for people with severe physical disabilities. In this case study, we propose a brain controlled wheelchair navigation system that can help the user travel to a desired destination, without having to personally drive the wheelchair and frequently change the movement directions along the path to the destination. The user can choose the desired destination from a map of the environment, using his/her brain signals only. The user can navigate through the map using BCI cognitivecommands. The system processes the brain signals, determines the required destination on the map, and constructs an optimized movement path from the source to the intended destination. To construct an obstacle-free path with the shortest possible distance and minimum number of turns, a path planning optimization problem is solved using a simple Simulated Annealing (SA) algorithm. The resulting optimized path will be translated into movement directions that are sent to the microcontroller to move the wheelchair to the desired destination. © 2014 Springer International Publishing.

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APA

Alrajhi, W., Hosny, M., Al-Wabil, A., & Alabdulkarim, A. (2014). Human factors in the design of BCI-controlled wheelchairs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8511 LNCS, pp. 513–522). Springer Verlag. https://doi.org/10.1007/978-3-319-07230-2_49

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