Isolating Discriminant Neural Activity in the Presence of Eye Movements and Concurrent Task Demands

• Published in: Frontiers in human neuroscience 2017-07, Vol.11, p.357-357
• Main Authors: Touryan, Jon, Lawhern, Vernon J, Connolly, Patrick M, Bigdely-Shamlo, Nima, Ries, Anthony J
• Format: Article
• Language: English
• Subjects: Brain research; Classification; Cognitive ability; cognitive load; EEG; Electroencephalography; Eye; Eye movements; eye tracking; fixation-related potentials; Information processing; Methods; Neuroscience; Paradigms; Signal processing; Studies; target detection; Visual perception
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2017.00357

A growing number of studies use the combination of eye-tracking and electroencephalographic (EEG) measures to explore the neural processes that underlie visual perception. In these studies, fixation-related potentials (FRPs) are commonly used to quantify early and late stages of visual processing that follow the onset of each fixation. However, FRPs reflect a mixture of bottom-up (sensory-driven) and top-down (goal-directed) processes, in addition to eye movement artifacts and unrelated neural activity. At present there is little consensus on how to separate this evoked response into its constituent elements. In this study we sought to isolate the neural sources of target detection in the presence of eye movements and over a range of concurrent task demands. Here, participants were asked to identify visual targets (Ts) amongst a grid of distractor stimuli (Ls), while simultaneously performing an auditory N-back task. To identify the discriminant activity, we used independent components analysis (ICA) for the separation of EEG into neural and non-neural sources. We then further separated the neural sources, using a modified measure-projection approach, into six regions of interest (ROIs): occipital, fusiform, temporal, parietal, cingulate, and frontal cortices. Using activity from these ROIs, we identified target from non-target fixations in all participants at a level similar to other state-of-the-art classification techniques. Importantly, we isolated the time course and spectral features of this discriminant activity in each ROI. In addition, we were able to quantify the effect of cognitive load on both fixation-locked potential and classification performance across regions. Together, our results show the utility of a measure-projection approach for separating task-relevant neural activity into meaningful ROIs within more complex contexts that include eye movements.