Decoding of top-down cognitive processing for SSVEP-controlled BMI
- Authors
- Min, Byoung-Kyong; Daehne, Sven; Ahn, Min-Hee; Noh, Yung-Kyun; Mueller, Klaus-Robert
- Issue Date
- 3-Nov-2016
- Publisher
- NATURE PUBLISHING GROUP
- Citation
- SCIENTIFIC REPORTS, v.6
- Indexed
- SCIE
SCOPUS
- Journal Title
- SCIENTIFIC REPORTS
- Volume
- 6
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/86866
- DOI
- 10.1038/srep36267
- ISSN
- 2045-2322
- 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.
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Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
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