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Cited by 654EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyNeural dynamics and brain function

A Survey of Stimulation Methods Used in SSVEP-Based BCIs

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Zhu, Danhua, Bieger, Jordi, Garcia Molina, Gary, Aarts, Ronald M.·2010·Computational Intelligence and Neuroscience
DOI 10.1155/2010/702357PMC PMC2833411
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Abstract

Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

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  • SSVEPs can be measured from the EEG at the occipital cortex during periodic presentation of visual stimuli, with a latency of 100 ms.

    “Steady-state visual evoked potentials (SSVEPs) are also widely used, and can be measured from the EEG at the occipital cortex during periodic presentation of visual stimuli [108], with a latency of 100 ms.”

    From Hands-Free Accessible Digital Musical Instruments: Conceptual Framework, Challenges, and Perspectives (2020) · cites this paper as [108]

    The Introduction says repetitive visual stimuli elicit SSVEPs that appear in EEG signals, especially those from the primary visual cortex. Section 5.2.3 also describes SSVEP signals obtained at the occipital lobes. The paper does not give a 100 ms latency; its references to 100 concern stimulus frequency, not latency. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Authors
Zhu, D., Bieger, J., Garcia Molina, G., & Aarts, R. M.
Affiliations
  • Philips (Netherlands) (NL)
  • Radboud University Nijmegen (NL)
  • Department of Brain, Body & Behavior, Philips Research Eindhoven, 5656 AE Eindhoven, The Netherlands
Corresponding author
Zhu, Danhua
ORCID
Venue
Computational Intelligence and Neuroscience
Publisher
Wiley
Published
2010
Volume
2010
Pages
1-12
ISSN
  • 1687-5265 (ISSN-L)
  • 1687-5273
DOI
10.1155/2010/702357
PMC
PMC2833411
OpenAlex
W2169296125
Cited by
654
References
1
Citations by year
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FWCI
2.70
Type
journal article
Language
en
Open access
Licence
Locations
  • Computational Intelligence and Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • TU/e Research Portal (publishedVersion, other-oa)
  • TU/e Research Portal (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • TU/e Research Portal (submittedVersion, other-oa)
Funders
Tsinghua University
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
steady-state visual evoked potentials, brain-computer interface, modulated light source
MeSH terms
Brain, Computers, Electroencephalography, Equipment Design, Evoked Potentials, Visual, Humans, Mental Processes, Photic Stimulation, Signal Processing, Computer-Assisted, Software, User-Computer Interface, Visual Perception

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