Skip to main content

Research Repository

Advanced Search

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

Fahrenfort, Johannes Jacobus; Grubert, Anna; Olivers, Christian N.L.; Eimer, Martin

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


Johannes Jacobus Fahrenfort

Christian N.L. Olivers

Martin Eimer


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.


Fahrenfort, J. J., Grubert, A., Olivers, C. N., & Eimer, M. (2017). Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection. Scientific Reports, 7, Article 1886.

Journal Article Type Article
Acceptance Date Apr 3, 2017
Online Publication Date May 15, 2017
Publication Date May 15, 2017
Deposit Date May 26, 2017
Publicly Available Date May 30, 2017
Journal Scientific Reports
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 7
Article Number 1886


Published Journal Article (1.9 Mb)

Publisher Licence URL

Copyright Statement
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

You might also like

Downloadable Citations