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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Data are available from the Dataverse database. Received: AugAccepted: MaPublished: April 13, 2017Ĭopyright: © 2017 Speier et al. The improvements achieved by these methods are therefore complementary and a combination yields superior results to either method implemented individually when tested in healthy subjects.Ĭitation: Speier W, Deshpande A, Cui L, Chandravadia N, Roberts D, Pouratian N (2017) A comparison of stimulus types in online classification of the P300 speller using language models. In online trials using the particle filter method, all 10 subjects achieved a higher selection rate when using the famous faces flashing paradigm than when using inverting flashes. In offline analysis using a previously published particle filter method, famous faces stimuli yielded superior results to both standard and inverting stimuli. Second, we test these methods with language model integration to assess whether different optimization approaches can be combined to further improve BCI communication. First, we aim to compare the famous faces stimulus paradigm with an existing alternative stimulus paradigm currently used in commercial systems (i.e., character inversion). The goal of this study is therefore twofold. While both have been shown separately to provide significant improvements, the two methods have not yet been implemented together to demonstrate that the improvements are complimentary. There are many parallel lines of research underway to overcome the system’s low signal to noise ratio and thereby improve performance, including using famous face stimuli and integrating language information into the classifier. The P300 Speller is a common brain-computer interface communication system.