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

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 of our model. Our findings demonstrate the power of multivariate EEG analysis to track feature-based target selection with high spatial and temporal precision.

Spatial specificity of alpha oscillations in the human visual system

“…During the delay period, in the absence of an external stimulus, the spatial distribution of alpha activity reflected the participant's attended location in the visual field. In line with previous electrophysiological studies (Bahramisharif et al, 2010;Cicmil, Bridge, Parker, Woolrich, & Krug, 2014;Fahrenfort, Grubert, Olivers, & Eimer, 2017;Hagler Jr. et al, 2009;Kelly et al, 2006;Kelly, Gomez-Ramirez, & Foxe, 2009;Perry et al, 2011;Rihs et al, 2007), the present report points to a novel mechanism on the relevance of alpha activity in allocating neurocomputational resources in a spatial attention task. This mechanism relates to the notion that the spatial variability of alpha power reflects a "moving spot" of reduced alpha (Jensen, Gips, Bergmann, & Bonnefond, 2014;.…”

Humans can efficiently look for but not select multiple visual objects

“…As shown in Figure S1 (and the SI Results), more traditional event-related analyses indeed revealed N2pc and SPCN components, which likely contributed to the classifiers' performance. For vertically positioned targets a gradient from frontal to posterior channels spread along the midline, similar to recent results from our labs (31,42). The fact that the decoding approach picks up on information related to attentional selection also on…”

“…However, because the N2pc can only distinguish between the left versus right hemifield, it is not able to simultaneously track the selection of multiple targets at different locations in more complex visual search displays. To overcome this limitation, we used multivariate decoding, which has been proven to successfully track the spatiotemporal dynamics of feature-based attentional RUNNING TITLE: COMPETITION IN MULTIPLE TARGET SELECTION selection at any location in a search display (31). This allowed us to independently track attentional selection over time for multiple concurrent targets at once, and also to investigate the parallel versus serial nature of these selection processes.…”

Spatially Selective Alpha Oscillations Reveal Moment-by-Moment Trade-offs between Working Memory and Attention

“…Our data suggest that the topographic distribution of theta activity not only carries hemifield-specific information but can also track the specific location that is selected. As has been argued by Fahrenfort, Grubert, Olivers, and Eimer (2017), spatial IEMs therefore provide a promising extension to N2pc paradigms, because researchers are no longer limited to the lateralized design.…”