July 2013
Volume 13, Issue 9
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Camera-based eye tracking improves the signal-to-noise ratio of EEG
Author Affiliations
  • Jason Satel
    Faculty of Computer Science, Dalhousie University
  • Cameron Hassall
    Department of Psychology, Faculty of Science, Dalhousie University
  • Olav Krigolson
    Department of Psychology, Faculty of Science, Dalhousie University
  • Raymond Klein
    Department of Psychology, Faculty of Science, Dalhousie University
Journal of Vision July 2013, Vol.13, 794. doi:https://doi.org/10.1167/13.9.794
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      Jason Satel, Cameron Hassall, Olav Krigolson, Raymond Klein; Camera-based eye tracking improves the signal-to-noise ratio of EEG. Journal of Vision 2013;13(9):794. https://doi.org/10.1167/13.9.794.

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      © ARVO (1962-2015); The Authors (2016-present)

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To examine event-related potentials (ERPs) - the brain responses associated with specific sensory, cognitive, and motor processes - researchers record brain activity using electroencephalography (EEG) while participants perform carefully designed tasks. EEG is inherently noisy, hence ERPs are derived by averaging over many trials. Since eye movements and blinks generate large electrical signals that contaminate EEG, methods have been developed to deal with these artifacts. Electrooculography uses activity from electrodes near the eyes to identify and remove contaminated trials, or participants who move their eyes too often. This method reduces reduces the signal-to-noise ratio and/or increases the number of participants required. Mathematical techniques, such as independent component analysis, have been used to allegedly remove eye movement activity. However, it is unclear whether all such activity is definitively removed, while leaving the neural activity of interest unaltered. Moreover, this approach necessarily includes trials with eye movements, often contrary to task requirements. In four recent experiments combining camera-based eye tracking and EEG, we demonstrated the potential of combining these technologies to increase ERP data quality. We used a cueing task where participants maintained fixation, ignored or made eye movements to uninformative exogenous or endogenous cues, then made manual localization responses to targets. Eye tracking ensured appropriate oculomotor behavior at all times. Error messages were presented when incorrect eye movements were observed and such trials were recycled. The ERPs were much cleaner (enhanced signal-to-noise ratio) here, relative to similar experiments without concurrent eye tracking. There are at least two (not mutually exclusive) explanations for this observation: a) participants learn to learn to control their oculomotor behavior through online feedback, minimizing untoward blinks and eye movements, and b) eye monitoring allows more accurate categorization of trials to be excluded than traditional techniques.

Meeting abstract presented at VSS 2013


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