Abstract
P300-based BCIs have been proposed as potential communication alternatives for individuals whose severe neuro-muscular limitations, e.g. due to amyotrophic lateral sclerosis (ALS), preclude their use of most commercially available assistive technologies. However, BCIs are currently limited by their relatively slower selection rates due to the significant amount of data collection required to improve the signal-to- noise ratio (SNR) of the elicited brain responses to achieve desired system accuracy levels. The conventional strategy is to average over a fixed amount of data prior to BCI decision making, an approach that might be inefficient given the inherent variation in a user’s responses during BCI use. There is need for the development of improved algorithms that can demonstrate increased performance in online testing, especially in target end-user populations. We have developed an algorithm that uses a Bayesian approach to collect only the amount of data necessary to reach a specified confidence level in the BCI’s decision based on continuous evaluation of the quality of a user’s responses. We further optimized the algorithm by incorporating statistical information about the user’s language. Results from online testing in participants with ALS demonstrate that using the Bayesian dynamic stopping algorithm resulted in a significant reduction in character selection time with minimal effect on accuracy, compared to conventional static data collection. In post-use surveys, the participants overwhelmingly preferred the dynamic stopping algorithms.
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This research project was funded by NIH/NIDCD under grant number R33 DC010470.
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Mainsah, B. et al. (2015). ALS Population Assessment of a Dynamic Stopping Algorithm Implementation for P300 Spellers. In: Guger, C., Müller-Putz, G., Allison, B. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-25190-5_8
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DOI: https://doi.org/10.1007/978-3-319-25190-5_8
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