2026-05-30T05:05:39Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

74fa6a5bd8d6db6d9819ddf68ffd64cc4d0eea85a9618c904ba4e113cc122522 2025-12-08T02:05:30Z language:enopenaire_data

Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing. Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing. doi:10.17026/DANS-ZTH-37CReasy-dataset:61472 P.W.M. Desain J. Thielen 2015-06-07 DANS Data Station Social Sciences and Humanities 2015-06-07 Social SciencesBrain Computer InterfaceVisual Matrix SpellerBroad Band Visually Evoked PotentialsReconvolutionElectroencephalographyPlosOne 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). https://ssh.datastations.nl/oai doi:10.17026/DANS-ZTH-37CR 2025-12-03T01:01:33Z ddi:codebook:2_5