Correlated Components of Ongoing EEG Point to Emotionally Laden Attention – A Possible Marker of Engagement?

Abstract

Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from multiple EEG records. The resulting components capture correlations down to a one-second time resolution, thus revealing that peak correlations of neural activity across viewings can occur in remarkable correspondence with arousing moments of the film. Moreover, a significant reduction in neural correlation occurs upon a second viewing of the film or when the narrative is disrupted by presenting its scenes scrambled in time. We also probe oscillatory brain activity during periods of heightened correlation, and observe during such times a significant increase in the theta band for a frontal component and reductions in the alpha and beta frequency bands for parietal and occipital components. Low-resolution EEG tomography of these components suggests that the correlated neural activity is consistent with sources in the cingulate and orbitofrontal cortices. Put together, these results suggest that the observed synchrony reflects attention- and emotion-modulated cortical processing which may be decoded with high temporal resolution by extracting maximally correlated components of neural activity.

Multiview Bayesian Correlated Component Analysis

“…This stronger universality assumption can potentially increase sensitivity as it involves estimation of fewer parameters. In correlated component analysis the weights are thus estimated through a single eigenvalue problem (Dmochowski et al, 2012),…”

“…CorrCA and CCA are designed to handle two views at a time. In case of multiple views we employed the scheme for combination proposed in Dmochowski et al (2012), i.e., the views are concatenated in time so that all pairwise combinations are compared. This method has the disadvantage that the number of samples in the concatenated data scales with the number of views as M (M − 1).…”

“…This is based in a rather strong assumption of representational universality, namely that both the extracted information (what) and spatial networks (where) are shared among subjects. To exploit the full spatio-temporal patterns of correlation and increase sensitivity, a multivariate version of this approach, so-called correlated component analysis (here abbreviated as CorrCA) was recently proposed by Dmochowski et al (2012). The method was further developed and applied in Dmochowski et al (2014).…”

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ABSTRACTCorrelated component analysis as proposed by Dmochowski et al (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical.

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Emerging methods for conceptual modelling in neuroimaging

“…Wehbe et al (2014b) applies non-linear effects and deep learning to a similar paradigm. Approaches such as these are now starting to be applied to electrophysiological data also (Dmochowski et al 2012;Wehbe et al 2014b) which enables analyses at the temporal scale of natural language (in spontaneous speech we typically produce 3-4 words a second). And finally the modelling of the idiosyncratic semantic spaces of individual participants (Charest et al 2014) demonstrates that the subtle differences in concept meaning seen in different languages may be open to analysis, despite crosslanguage similarities in neural representations (Buchweitz et al 2012;Zinszer et al 2016) These tools continue to provide further evidence on the amodal (Fairhall et al, 2013;Liuzzi et al, 2015) or embodied (Fernandino et al, 2015Anderson et al, 2016) encoding of meaning of the brain.…”