Abstract
An experiment was conducted to explore the feasibility of using physiological indicators (i.e. eye-tracking and electroencephalography [EEG]) to drive identification of relevant areas of interest during imagery analysis. Results indicate that ocular fixations are longer when a target is believed to be present. Furthermore, the accuracy of correct identification of targets could be identified based on fixation duration, given that fixations were significantly longer when a target was actually present. In addition, by synching eye-tracking fixation points to EEG, fixation-locked event-related potentials (FLERPs) show potential for detecting distinctive patterns and scalp distributions for various types of fixations, which may be used to classify fixation points based on level of interest. This paper reports findings from a study and summarizes challenges and implications for constructing a system where eye tracking is used to drive EEG ERP evaluation of interest during a defined search task within complex static images.
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