Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection

Scientific Reports
Johannes Jacobus FahrenfortMartin Eimer

Abstract

The primary electrophysiological marker of feature-based selection is the N2pc, a lateralized posterior negativity emerging around 180-200 ms. As it relies on hemispheric differences, its ability to discriminate the locus of focal attention is severely limited. Here we demonstrate that multivariate analyses of raw EEG data provide a much more fine-grained spatial profile of feature-based target selection. When training a pattern classifier to determine target position from EEG, we were able to decode target positions on the vertical midline, which cannot be achieved using standard N2pc methodology. Next, we used a forward encoding model to construct a channel tuning function that describes the continuous relationship between target position and multivariate EEG in an eight-position display. This model can spatially discriminate individual target positions in these displays and is fully invertible, enabling us to construct hypothetical topographic activation maps for target positions that were never used. When tested against the real pattern of neural activity obtained from a different group of subjects, the constructed maps from the forward model turned out statistically indistinguishable, thus providing independent validation ...Continue Reading

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Citations

Oct 19, 2017·Journal of Cognitive Neuroscience·Dirk van MoorselaarEdward Awh
Mar 1, 2019·Psychological Science·Gi-Yeul Bae, Steven J Luck
Aug 28, 2019·ELife·Eduard OrtChristian Nl Olivers
Jul 11, 2019·Human Brain Mapping·Tzvetan PopovOle Jensen
Jul 19, 2018·Frontiers in Neuroscience·Johannes J FahrenfortChristian N L Olivers
Sep 19, 2018·Scientific Reports·Thiago Leiros CostaPaulo Sérgio Boggio
Jan 29, 2021·Journal of Neuroscience Methods·Joram van DrielJohannes J Fahrenfort

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Software Mentioned

MATLAB
EEGLAB
Windows XP
Amsterdam Decoding and Modeling ( ADAM ) toolbox
BrainVision
FieldTrip

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