ArticleScientific reports2017
Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- A comprehensive study of template-based frequency detection methods in SSVEP-based brain-computer interfaces.Behavior research methods · 2025Article
- VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials.Sensors (Basel, Switzerland) · 2023Article
- Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs.Scientific reports · 2022Article
- Effortless retaliation: the neural dynamics of interpersonal intentions in the Chicken Game using brain-computer interface.Social cognitive and affective neuroscience · 2021Article
- Asynchronous c-VEP communication tools-efficiency comparison of low-target, multi-target and dictionary-assisted BCI spellers.Scientific reports · 2020Article
- [Influence of the concrete and abstract graphs on N200 and P300 potentials].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- Five Shades of Grey: Exploring QuintaryComputational intelligence and neuroscience · 2020Article
- Article
- Article
- Accurate Decoding of Short, Phase-Encoded SSVEPs.Sensors (Basel, Switzerland) · 2018Article
- Decoding Steady-State Visual Evoked Potentials From Electrocorticography.Frontiers in neuroinformatics · 2018Article
- A multi-target brain-computer interface based on code modulated visual evoked potentials.PloS one · 2018Article
- Enhancing Performance and Bit Rates in a Brain-Computer Interface System With Phase-to-Amplitude Cross-Frequency Coupling: Evidences From Traditional c-VEP, Fast c-VEP, and SSVEP Designs.Frontiers in neuroinformatics · 2018Article
- Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing.Frontiers in neuroscience · 2017Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target. Especially for a small number of repetitions of the coding sequence, our beamforming approach significantly outperforms an optimised support vector machine (SVM)-based classifier, which is considered state-of-the-art in cVEP-based BCI. In addition to the traditional 60 Hz stimulus presentation rate for the coding sequence, we also explore the 120 Hz rate, and show that the latter enables faster communication, with a maximal median ITR of 172.87 bits/min. Finally, we also report on a transition effect in the EEG signal following the onset of the stimulus sequence, and recommend to exclude the first 150 ms of the trials from decoding when relying on a single presentation of the stimulus sequence.
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