ENHANCING BCI PERFORMANCE THROUGH COLLABORATION OF EYE GAZE AND SSVEP

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages159-161
Number of pages3
Publication statusPublished - 19 Jul 2017
EventNeuroadaptive Technologies 2017 - Berlin
Duration: 19 Jul 2017 → …

Conference

ConferenceNeuroadaptive Technologies 2017
Period19/07/17 → …

Fingerprint

information exchange
eyes
decision making

Keywords

  • BCI

Cite this

@inproceedings{926a3414842a44bc9d03be2c3a303f9c,
title = "ENHANCING BCI PERFORMANCE THROUGH COLLABORATION OF EYE GAZE AND SSVEP",
abstract = "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.",
keywords = "BCI",
author = "Chris Brennan and McCullagh, {P. J.} and Leo Galway and Gaye Lightbody",
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] {\'E}vain, A., Argelaguet, F., Casiez, G., Roussel, N., & L{\'e}cuyer, 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",
year = "2017",
month = "7",
day = "19",
language = "English",
isbn = "N/A",
pages = "159--161",
booktitle = "Unknown Host Publication",

}

Brennan, C, McCullagh, PJ, Galway, L & Lightbody, G 2017, ENHANCING BCI PERFORMANCE THROUGH COLLABORATION OF EYE GAZE AND SSVEP. in Unknown Host Publication. pp. 159-161, Neuroadaptive Technologies 2017, 19/07/17.

ENHANCING BCI PERFORMANCE THROUGH COLLABORATION OF EYE GAZE AND SSVEP. / Brennan, Chris; McCullagh, P. J.; Galway, Leo; Lightbody, Gaye.

Unknown Host Publication. 2017. p. 159-161.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - ENHANCING BCI PERFORMANCE THROUGH COLLABORATION OF EYE GAZE AND SSVEP

AU - Brennan, Chris

AU - McCullagh, P. J.

AU - Galway, Leo

AU - Lightbody, Gaye

N1 - 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

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N2 - 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.

AB - 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.

KW - BCI

M3 - Conference contribution

SN - N/A

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EP - 161

BT - Unknown Host Publication

ER -