Performance of a Steady-State Visual Evoked Potential and Eye Gaze Hybrid Brain-Computer Interface on Participants With and Without a Brain Injury

Chris Brennan, P McCullagh, Gaye Lightbody, Leo Galway, Sally I McClean, Piotr Stawicki, Felix Gembler, Ivan Volosyak, Elaine Armstrong, Eileen Thompson

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Abstract

The brain-computer interface (BCI) and the tracking of eye gaze provide modalities for human-machine communication and control. In this article, we provide the evaluation of a collaborative BCI and eye gaze approach, known as a hybrid BCI. The combined inputs interact with a virtual environment to provide actuation according to a four-way menu system. The following two approaches are evaluated: first, steady-state visual evoked potential (SSVEP) BCI with on-screen stimulation; second, hybrid BCI, which combined eye gaze and SSVEP for navigation and selection. A study comprises participants without known brain injury (non-BI, N = 30) and participants with known brain injury (BI, N = 14). A total of 29 out of 30 non-BI participants can successfully control the hybrid BCI, while nine out of the 14 BI participants are able to achieve control, as evidenced by task completion. The hybrid BCI provides a mean accuracy of 99.84% in the cohort of non-BI participants and 99.14% in the cohort of BI participants. Information transfer rates are 24.41 bpm in non-BI participants and 15.87 bpm in BI participants. The research goal is to quantify usage of SSVEP and ET approaches in cohorts of non-BI and BI participants. The hybrid is the preferred interaction modality for most participants for both cohorts. When compared to non-BI participants, it is encouraging that nine out of 14 participants with known BI can use the hBCI technology with equivalent accuracy and efficiency, albeit with slower transfer rates.

Original languageEnglish
Article number9078355
Pages (from-to)277-286
Number of pages10
JournalIEEE Transactions on Human-Machine Systems
Volume50
Issue number4
Early online date24 Apr 2020
DOIs
Publication statusPublished - 1 Aug 2020

Keywords

  • Brain–computer interface (BCI)
  • brain injury (BI)
  • data fusion
  • eye tracking
  • virtual environment

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