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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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Cites
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
(2005) · Cited by 5,977
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
Self-citationThe distinct modes of vision offered by feedforward and recurrent processing
Lamme, V. A. F., & Roelfsema, P. R. (2000) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01657-x · Cited by 2,338
Temporary suppression of visual processing in an RSVP task: An attentional blink?
Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.18.3.849 · Cited by 2,286
Analyzing Neural Time Series Data: Theory and Practice
Cohen, M. X. (2014) · doi:10.7551/mitpress/9609.001.0001 · Cited by 2,105
Temporary suppression of visual processing in an RSVP task: an attentional blink?
Raymond, J. E., Shapiro, K. L., & Arnell, K. M. (1992) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/0096-1523.18.3.849 · Cited by 1,649
Toward Direct Brain-Computer Communication
Vidal, J. J. (1973) · Annual Review of Biophysics and Bioengineering · doi:10.1146/annurev.bb.02.060173.001105 · Cited by 1,311
Event-related potential studies of attention.
Luck, S., Woodman, G., & Vogel, E. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01545-x · Cited by 1,209
A Metric for Thought: A Comparison of P300 Latency and Reaction Time
McCarthy, G., & Donchin, E. (1981) · Science · doi:10.1126/science.7444452 · Cited by 1,147
Research works printed but not linked: 22
- Bohannon A W, 2017, 2016 IEEE Int. Conf. on Systems, Man, and Cybernetics, SMC 2016—Conf. Proc., 4046
- Cecotti H, 2011, Conf. Proc., Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conf., 6381
- Cecotti H, 2011, Conf. Proc., Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conf., 6311
- Cecotti H, 2012, Conf. Proc., Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conf., 1723
- Erdogmus D, 2006, 2006 IEEE Int. Joint Conf. on Neural Network Proc., 1136
- Healy G, 2011, Proc. of HCI 2011—25th BCS Conf. on Human Computer Interaction, 157
- Hild K E, 2011, ACL HLT 2011—49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of Student Session, 38
- Huang Y, 2006, 2006 IEEE Int. Joint Conf. on Neural Network Proc., 1136
- Huang Y, 2007, Proc. of the 3rd Int. IEEE EMBS Conf. on Neural Engineering, 473
- Huang Y, 2008, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 429
- Kindermans P-J, 2014, J. Neural Eng., 11
- Marathe A, 2015, IEEE Trans. Neural Syst. Rehabil. Eng., 4320, 1
- Matran-Fernandez A, 2014, 6th Computer Science and Electronic Engineering Conf. CEEC 2014—Conf. Proc., 127
- Orhan U, 2011, Conf. Proc. Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conf., 5774
- Orhan U, 2012, Conf. Proc., Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conf., 2497
- Orhan U, 2012, ICASSP, IEEE Int. Conf. on Acoustics, Speech and Signal Processing—Proc., 645
- Rensink R A, 2000, Change, 6, 458
- Saavedra C, 2012, NeuroComp/KEOpS’12 Workshop
- 10.1109/JSTSP.2014.2313022 · DOI
- Sajda P, 2003, Proc. 1st Int. IEEE EMBS Conf. on Neural Engineering, 7
- Zander T O, 2011, J. Neural Eng., 8
- Zhang Y, 2012, J. Neural Eng., 9
Cited by
Sorted by citations
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016
Wu, D., Xu, Y., & Lu, B.-L. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3007453 · Cited by 434
Interface, interaction, and intelligence in generalized brain–computer interfaces
Gao, X., Wang, Y., Chen, X., & Gao, S. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.04.003 · Cited by 324
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296
Chen, Z., Liu, X., Kojima, M., Huang, Q., & Arai, T. (2021) · Sensors · doi:10.3390/s21041536 · Cited by 76
Gu, X., Cao, Z., Jolfaei, A., Xu, P., Wu, D., Jung, T., & Lin, C. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3052811 · Cited by 67
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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