Speed of Rapid Serial Visual Presentation of Pictures, Numbers and Words Affects Event-Related Potential-Based Detection Accuracy

Stephanie Lees, P McCullagh, Phillippa Payne, Liam Maguire, Fabien Lotte, Damien Coyle

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Rapid serial visual presentation (RSVP) based brain-computer interfaces (BCIs) can detect target images among a continuous stream of rapidly presented images, by classifying a viewer’s event related potentials (ERPs) associated with the target and non-targets images. Whilst the majority of RSVP-BCI studies to date have concentrated on the identification of a single type of image, namely pictures, here we study the capability of RSVP-BCI to detect three different target image types: pictures, numbers and words. The impact of presentation duration (speed) i.e., 100-200ms (5-10Hz), 200-300ms (3.3-5Hz) or 300-400ms (2.5-3.3Hz), is also investigated. 2-way repeated measure ANOVA on accuracies of detecting targets from non-target stimuli (ratio 1:9) measured via area under the receiver operator characteristics curve (AUC) for N=15 subjects revealed a significant effect of factor Stimulus-Type (pictures, numbers, words) (F (2,28) = 7.243, p = 0.003) and for Stimulus-Duration (F (2,28) = 5.591, p = 0.011). Furthermore, there is an interaction between stimulus type and duration: F (4,56) = 4.419, p = 0.004). The results indicate that when designing RSVP-BCI paradigms, the content of the images and the rate at which images are presented impact on the accuracy of detection and hence these parameters are key experimental variables in protocol design and applications, which apply RSVP for multimodal image datasets.
Original languageEnglish
Article number8903477
Pages (from-to)113-122
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number1
Early online date18 Nov 2019
Publication statusPublished (in print/issue) - 8 Jan 2020

Bibliographical note

Funding Information:
This work was supported by the U.K. Government through the Anglo-French Ph.D. Programme.
(Corresponding author: Stephanie Lees.) S. Lees, P. McCullagh, L. Maguire, and D. Coyle are with the Intelligent Systems Research Centre, Faculty of Computing, Engineering and the Built Environment, Ulster University, Londonderry BT48 7JL, U.K. P. Payne is with Cranfield University, Bedford MK43 0AL, U.K.

Publisher Copyright:
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Copyright 2020 Elsevier B.V., All rights reserved.


  • BCI
  • EEG
  • Rapid serial visual presentation
  • brain-computer interface
  • electroencephalography
  • event related potentials


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