Chris Brennan, P. J. McCullagh, Leo Galway, Gaye Lightbody

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


A hybrid BCI (hBCI) based on SSVEP and eye tracking enhanced interaction performance in terms of Accuracy (Acc.), Efficiency (Eff.) and Information Transfer Rate (ITR) in 29 of 30 participants, when compared to SSVEP alone. Decisions were based on collaborative processing. The SSVEP component was used for selection, reinforcing the eye gaze and solving the ‘Midas touch’ problem associated with eye gaze alone. The overall arithmetic mean Acc., Eff., and ITR for 29 participants completing the four (4-way navigation) tasks was 99.84% (±0.77%), 99.74% (±1.23%) and 24.41 (±6.35) bits/min, respectively. Review of the data shows that adaption of the decision process is possible; this would increase ITR and hence usability of the technology and provide further insight into the decision-making process.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherNeuroadaptive Technology
Number of pages3
ISBN (Print)N/A
Publication statusPublished - 19 Jul 2017
EventNeuroadaptive Technologies 2017 - Berlin
Duration: 19 Jul 2017 → …


ConferenceNeuroadaptive Technologies 2017
Period19/07/17 → …

Bibliographical note

Reference text: REFERENCES
[1] Pfurtscheller, G., Allison, B. Z., Brunner, C., Bauernfeind, G., Solis-Escalante, T., Scherer, R., … Birbaumer, N. (2010). The hybrid BCI. Frontiers in Neuroscience, 4(April), 30
[2] Choi, I., Rhiu, I., Lee, Y., Yun, M. H., & Nam, C. S. (2017). A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives. PloS One, 12(4), e0176674. http://doi.org/10.1371/journal.pone.0176674
[3] Allison, B., Jin, J., Zhang, Y., & Wang, X. (2014). A four-choice hybrid P300/SSVEP BCI for improved accuracy. Brain-Computer Interfaces, 1(1), 17–26
[4] Zander, T. O., Gaertner, M., Kothe, C., & Vilimek, R. (2010). Combining Eye Gaze Input With a Brain–Computer Interface for Touchless Human–Computer Interaction. International Journal of Human-Computer Interaction, 27(1), 38–51
[5] Évain, A., Argelaguet, F., Casiez, G., Roussel, N., & Lécuyer, A. (2016). Design and Evaluation of Fusion Approach for Combining Brain and Gaze Inputs for Target Selection. Frontiers in Neuroscience, 10, 454. http://doi.org/10.3389/fnins.2016.00454
[6] Kosmyna, N., Tarpin-Bernard, F., Bonnefond, N., & Rivet, B. (2016). Feasibility of BCI Control in a Realistic Smart Home Environment. Frontiers Human Neuroscience, 10(416), 10
[7] Valbuena D, Volosyak I and Graser A (2010) sBCI: fast detection of steady-state visual evoked potentials Proc.IEEE EMBC’2010


  • BCI


Dive into the research topics of 'ENHANCING BCI PERFORMANCE THROUGH COLLABORATION OF EYE GAZE AND SSVEP'. Together they form a unique fingerprint.

Cite this