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PLoS One. 2017 Apr 26;12(4):e0176349. doi: 10.1371/journal.pone.0176349. eCollection 2017.

Pattern classification of EEG signals reveals perceptual and attentional states.

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Department of Psychology and Neuroscience Program, Hamilton College, Clinton, New York, United States of America.
Department of Psychology, Yale University, New Haven, Connecticut, United States of America.
Department of Cognitive Science, Occidental College, Los Angeles, California, United States of America.
Research Service, VA Boston Healthcare System, Boston, Massachusetts, United States of America.
Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, United States of America.


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 well-established) 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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