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A review of rapid serial visual presentation-based brain–computer interfaces
Open accessAbstract
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.
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“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].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [15]
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“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].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [15]
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Metadata
- 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
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- 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
- hybrid
- doi.org
- Licence
- 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
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