Cited by 149EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyNeural and Behavioral Psychology Studies

A review of rapid serial visual presentation-based brain–computer interfaces

Open access
Lees, Stephanie, Dayan, Natalie, Cecotti, Hubert, McCullagh, Paul, Maguire, Liam, Lotte, Fabien, Coyle, Damien·2018·Journal of Neural Engineering

Abstract

Rapid serial visual presentation (RSVP) combined with the detection of event-related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited, but significant, literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1088/1741-2552/aa9817

Cites

118 of 118 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In the RSVP-based P300 experiment, the target stimulus should be recognizably different and occupy 5%-10% of all stimuli.

    “In the RSVP-based P300 experiment, the participants need to distinguish between target and non-target stimuli where the target one should be recognizably different and occupy 5%-10% of all stimuli [15].”
  • RSVP-based BCI can be beneficial to applications where a large number of images need to be reviewed by professionals but cannot be well analyzed by computers.

    “Different from the P300 speller, the RSVP-based BCI can be beneficial to more applications where a large number of images need to be reviewed by professionals but are unable to be well analyzed by computers [15], [16], [17].”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

2 of the 120 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Lees, S., Dayan, N., Cecotti, H., McCullagh, P., Maguire, L., Lotte, F., & Coyle, D.
Affiliations
  • University of Ulster (GB)
  • California State University, Fresno (US)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Coyle, Damien
Venue
Journal of Neural Engineering
Published
2018-01-24
Volume
15
Issue
2
Pages
021001
ISSN
  • 1741-2552 (ISSN-L)
  • 1741-2560
OpenAlex
W2767056600
Cited by
149
References
120
Citations by year
2026: 7 · 2025: 24 · 2024: 35 · 2023: 21 · 2022: 14 · 2021: 18 · 2020: 16 · 2019: 8 · 2018: 6
FWCI
0.92
Type
journal article
Language
en
Open access
Locations
  • Journal of Neural Engineering (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Ulster University Research Portal (Ulster University) (publishedVersion, other-oa)
  • HAL (submittedVersion, other-oa)
Funders
Engineering and Physical Sciences Research Council (EP/H012958/1)
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 and Behavioral Psychology Studies
Keywords
rapid serial visual presentation, brain-computer interface, event-related potentials, preattentive processing, EEG, human-machine symbiosis, display modes, experimental standardization
MeSH terms
Brain, Electroencephalography, Evoked Potentials, Visual, Humans, Photic Stimulation, Time Factors, Brain-Computer Interfaces

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review