ArticleScientific reports2016
Decoding of top-down cognitive processing for SSVEP-controlled BMI.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 31 citations in OpenAlex.
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- Evidence of Serial Dependence from Decoding of Visual Evoked Potentials.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Optimal flickering light stimulation for entraining gamma rhythms in older adults.Scientific reports · 2022Article
- Automatic Speech Discrimination Assessment Methods Based on Event-Related Potentials (ERP).Sensors (Basel, Switzerland) · 2022Article
- Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.Cognitive neurodynamics · 2021Review
- A Hybrid Speller Design Using Eye Tracking and SSVEP Brain-Computer Interface.Sensors (Basel, Switzerland) · 2020Article
- Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance.Frontiers in neuroscience · 2020Article
- Electro-Encephalography and Electro-Oculography in Aeronautics: A Review Over the Last Decade (2010-2020).Frontiers in neuroergonomics · 2020Review
- Development of an electrooculogram-based human-computer interface using involuntary eye movement by spatially rotating sound for communication of locked-in patients.Scientific reports · 2018Article
- Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset.Scientific data · 2018Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 multi-class intentional control of EEG-BMIs without using gaze-shifting.
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