Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. Visual stimulation with pseudo-random bitsequences 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. In an online experiment, 12 participants tested a 6x6 matrix speller BCI. 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.

Note, these files contain processed data, used to create the figures as shown in the related article. The actual (raw) EEG data, recorded from human participants is available upon request (jordy.thielen@gmail.com, or info@donders.ru.nl).