Pattern classification of EEG signals reveals perceptual and attentional states
April
Pattern classification of EEG signals reveals perceptual and attentional states
Alexandra List 0 1 2
Monica D. Rosenberg 0 2
Aleksandra Sherman 0 2
Michael Esterman 0 2
0 Funding: National Institutes of Health R01 EY018197-02S1 (supported AL); National Science Foundation Graduate Research Fellowship (to MR); Veterans Affairs Clinical Science Research and Development Career Development Award 1IK2CX000706-01A2 (to ME). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript
1 Department of Psychology and Neuroscience Program, Hamilton College, Clinton, New York, United States of America, 2 Department of Psychology, Yale University , New Haven , Connecticut, United States of America, 3 Department of Cognitive Science, Occidental College , Los Angeles , California, United States of America, 4 Research Service, VA Boston Healthcare System, Boston, Massachusetts, United States of America, 5 Department of Psychiatry, Boston University School of Medicine , Boston, Massachusetts , United States of America
2 Editor: Lawrence M Ward, University of British Columbia , CANADA
Pattern classification techniques have been widely used to differentiate neural activity associated with different perceptual, attentional, or other cognitive states, often using fMRI, but more recently with EEG as well. Although these methods have identified EEG patterns (i.e., scalp topographies of EEG signals occurring at certain latencies) that decode perceptual and attentional states on a trial-by-trial basis, they have yet to be applied to the spatial scope of attention toward global or local features of the display. Here, we initially used pattern classification to replicate and extend the findings that perceptual states could be reliably decoded from EEG. We found that visual perceptual states, including stimulus location and object category, could be decoded with high accuracy peaking between 125±250 ms, and that the discriminative spatiotemporal patterns mirrored and extended our (and other wellestablished) ERP results. Next, we used pattern classification to investigate whether spatiotemporal EEG signals could reliably predict attentional states, and particularly, the scope of attention. The EEG data were reliably differentiated for local versus global attention on a trial-by-trial basis, emerging as a specific spatiotemporal activation pattern over posterior electrode sites during the 250±750 ms interval after stimulus onset. In sum, we demonstrate that multivariate pattern analysis of EEG, which reveals unique spatiotemporal patterns of neural activity distinguishing between behavioral states, is a sensitive tool for characterizing the neural correlates of perception and attention.
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Data Availability Statement: Data from the paper
are available through the Open Science Framework
at osf.io/nnke6.
Introduction
Over the last decade, multivariate pattern-classification analyses of fMRI BOLD signals have
emerged as a fruitful approach for using neural activity to decode various behavioral states
including perceiving, attending to, and imagining features, objects, and scenes (for reviews,
see [1±4]). Recently, pattern-classification analyses have also been applied to
electroencephalography (EEG) signals (e.g., [5±16]). This application to EEG has extended the standard
event-related potential (ERP) analyses in which a critical electrode (or a cluster of electrodes)
is selected within a specific scalp region (based on data inspection and/or prior results), and
the trial-averaged stimulus-evoked EEG signals (i.e., ERPs) from the selected electrode(s) are
compared between conditions. Instead, as applied here, multivariate classification techniques
can reveal, in an agnostic data-driven manner, topographic weightings of EEG signals that
maximally distinguish specific perceptual, attentional, or behavioral states within a given time
interval. Thus, pattern-classification analyses offer greater sensitivity than standard ERP
analyses by simultaneously integrating information across electrodes. Because pattern-classification
analyses identify EEG correlates with high sensitivity, they are typically evaluated by how well
they predict the corresponding perceptual, attentional, or behavioral states on a trial-by-trial
basis (rather than how well trial-averaged signals from selected electrodes differentiate
experimental conditions, as in standard ERP analyses). Cross-validated predictive measures, like the
ones we use here, are also less susceptible to false positives than analyses traditionally applied
to ERPs, because inaccurate models will not generalize to the held-out data.
The first aim of the current study is to replicate and extend prior EEG applications of
pattern-classification analyses toward decoding perceptual states. Although prior studies have
applied similar analyses toward classifying object category (e.g., faces versus cars), they have
done so in the (...truncated)