Decoding of top-down cognitive processing for SSVEP-controlled BMI
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Min, Byoung-Kyong | - |
dc.contributor.author | Daehne, Sven | - |
dc.contributor.author | Ahn, Min-Hee | - |
dc.contributor.author | Noh, Yung-Kyun | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.date.accessioned | 2021-09-03T17:05:21Z | - |
dc.date.available | 2021-09-03T17:05:21Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-11-03 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/86866 | - |
dc.description.abstract | We present a fast and accurate non-invasive brain-machine interface (BMI) based on demodulating steady-state visual evoked potentials (SSVEPs) in electroencephalography (EEG). Our study reports an SSVEP-BMI that, for the first time, decodes primarily based on top-down and not bottom-up visual information processing. The experimental setup presents a grid-shaped flickering line array that the participants observe while intentionally attending to a subset of flickering lines representing the shape of a letter. While the flickering pixels stimulate the participant's visual cortex uniformly with equal probability, the participant's intention groups the strokes and thus perceives a 'letter Gestalt'. We observed decoding accuracy of 35.81% (up to 65.83%) with a regularized linear discriminant analysis; on average 2.05-fold, and up to 3.77-fold greater than chance levels in multi-class classification. Compared to the EEG signals, an electrooculogram (EOG) did not significantly contribute to decoding accuracies. Further analysis reveals that the top-down SSVEP paradigm shows the most focalised activation pattern around occipital visual areas; Granger causality analysis consistently revealed prefrontal top-down control over early visual processing. Taken together, the present paradigm provides the first neurophysiological evidence for the top-down SSVEP BMI paradigm, which potentially enables multiclass intentional control of EEG-BMIs without using gaze-shifting. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.subject | ANTERIOR PREFRONTAL CORTEX | - |
dc.subject | BRAIN-COMPUTER INTERFACE | - |
dc.subject | SPATIAL ATTENTION | - |
dc.subject | VISUAL-ATTENTION | - |
dc.subject | STATE | - |
dc.subject | EXTRASTRIATE | - |
dc.subject | ACTIVATION | - |
dc.subject | COMPONENTS | - |
dc.subject | RESPONSES | - |
dc.subject | DESIGN | - |
dc.title | Decoding of top-down cognitive processing for SSVEP-controlled BMI | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Min, Byoung-Kyong | - |
dc.identifier.doi | 10.1038/srep36267 | - |
dc.identifier.scopusid | 2-s2.0-84994205122 | - |
dc.identifier.wosid | 000387343700001 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, v.6 | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.citation.title | SCIENTIFIC REPORTS | - |
dc.citation.volume | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | ANTERIOR PREFRONTAL CORTEX | - |
dc.subject.keywordPlus | BRAIN-COMPUTER INTERFACE | - |
dc.subject.keywordPlus | SPATIAL ATTENTION | - |
dc.subject.keywordPlus | VISUAL-ATTENTION | - |
dc.subject.keywordPlus | STATE | - |
dc.subject.keywordPlus | EXTRASTRIATE | - |
dc.subject.keywordPlus | ACTIVATION | - |
dc.subject.keywordPlus | COMPONENTS | - |
dc.subject.keywordPlus | RESPONSES | - |
dc.subject.keywordPlus | DESIGN | - |
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