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The Study of Generic Model Set for Reducing Calibration Time in P300-based Brain-Computer Interface.

07:00 EST 28th November 2019 | BioPortfolio

Summary of "The Study of Generic Model Set for Reducing Calibration Time in P300-based Brain-Computer Interface."

P300-based brain-computer interfaces (BCIs) provide an additional communication channel for individuals with communication disabilities. In general, P300-based BCIs need to be trained, offline, for a considerable period of time, which causes users to become fatigued. This reduces the efficiency and performance of the system. In order to shorten calibration time and improve system performance, we introduce the concept of a generic model set. We used ERP data from 116 participants to train the generic model set. The resulting set consists of ten models, which are trained by weighted linear discriminant analysis (WLDA). Twelve new participants were then invited to test the validity of the generic model set. The results demonstrated that all new participants matched the best generic model. The resulting mean classification accuracy equaled 80% after online training, an accuracy that was broadly equivalent to the typical training model method. Moreover, the calibration time was shortened by 70.7% of the calibration time of the typical model method. In other words, the best matching model method only took 81s to calibrate, while the typical model method took 276s. There were also significant differences in both accuracy and raw bit rate between the best and the worst matching model methods. We conclude that the strategy of combining the generic models with online training is easily accepted and achieves higher levels of user satisfaction (as measured by subjective reports). Thus, we provide a valuable new strategy for improving the performance of P300-based BCI.

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This article was published in the following journal.

Name: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
ISSN: 1558-0210
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Medical and Biotech [MESH] Definitions

A member of the p300-CBP transcription factors that was originally identified as a binding partner for ADENOVIRUS E1A PROTEINS.

A study that uses observations at multiple time points before and after an intervention (the "interruption"), in an attempt to detect whether the intervention has had an effect significantly greater than any underlying trend over time.

Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.

A late-appearing component of the event-related potential. P300 stands for a positive deflection in the event-related voltage potential at 300 millisecond poststimulus. Its amplitude increases with unpredictable, unlikely, or highly significant stimuli and thereby constitutes an index of mental activity. (From Campbell, Psychiatric Dictionary, 6th ed)

A family of histone acetyltransferases that is structurally-related to CREB-BINDING PROTEIN and to E1A-ASSOCIATED P300 PROTEIN. They function as transcriptional coactivators by bridging between DNA-binding TRANSCRIPTION FACTORS and the basal transcription machinery. They also modify transcription factors and CHROMATIN through ACETYLATION.

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