title: Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing.
abstract: Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.
authors.affiliation: Radboud University Nijmegen, Donders Center for Cognition, Nijmegen, Netherlands.
authors.forename: Jordy
authors.initials: J
authors.lastname: Thielen
authors.affiliation: Radboud University Nijmegen, Donders Center for Cognition, Nijmegen, Netherlands.
authors.forename: Philip
authors.initials: P
authors.lastname: van den Broek
authors.affiliation: Radboud University Nijmegen, Donders Center for Cognition, Nijmegen, Netherlands.
authors.forename: Jason
authors.initials: J
authors.lastname: Farquhar
authors.affiliation: Radboud University Nijmegen, Donders Center for Cognition, Nijmegen, Netherlands.
authors.forename: Peter
authors.initials: P
authors.lastname: Desain
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D005074:Evoked Potentials, Visual; D005260:Female; D006801:Humans; D020407:Internet; D008297:Male; D010775:Photic Stimulation; D015203:Reproducibility of Results; D055815:Young Adult