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To overcome this disadvantage, we employed a Bayesian approach in which the DL-BCI was modeled as a Binary phase shift keying receiver for which the accuracy can be estimated a priori as a function of the signal-to-noise ratio. The results showed the measured accuracy to match the predefined target accuracy, thus validating this model that made possible to estimate in advance the classification accuracy on a trial-by-trial basis. This constitutes a novel methodology in the design of fully auditory DL-BCIs that let us first, define the target accuracy for a specific application and second, classify when the signal-to-noise ratio guarantees that target accuracy. <\/jats:p>","DOI":"10.1142\/s0129065712500098","type":"journal-article","created":{"date-parts":[[2012,5,15]],"date-time":"2012-05-15T11:21:13Z","timestamp":1337080873000},"page":"1250009","source":"Crossref","is-referenced-by-count":34,"title":["AN AUDITORY BRAIN-COMPUTER INTERFACE WITH ACCURACY PREDICTION"],"prefix":"10.1142","volume":"22","author":[{"given":"M. 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