A multi-target c-VEP-based BCI speller utilizing n-gram word prediction and filter bank classification
作者： Felix Gembler , Mihaly Benda , Abdul Saboor , Ivan Volosyak
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摘要: Brain-Computer Interfaces (BCIs) based on code-modulated visual evoked potentials can be used as hand-free communication tool for severely disabled people. In this paper we propose a filter bank design c-VEP BCIs alpha, beta and gamma sub-bands. The approach was tested using dictionary driven spelling application utilizing flexible time-windows. graphical user interface offers word suggestions that are updated after each selection. system with 18 healthy participants. Performance of sentence task analyzed. Remarkably, in the task, all participants reached 100 % accuracy. mean accuracy still extremely high (97 %). Furthermore, to assess speed system, information transfer rate (ITR) output characters per minute (OCM) were calculated. Mean ITRs 149.3 bpm 93.1 spelling; OCM 29.9 chars/min 32.1 chars/min.
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