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 mult...

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Autores principales:	Johannes Jacobus Fahrenfort, Anna Grubert, Christian N. L. Olivers, Martin Eimer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/10df2ae668ad437a86a518a4533e52be
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spelling	oai:doaj.org-article:10df2ae668ad437a86a518a4533e52be2021-12-02T15:05:56ZMultivariate EEG analyses support high-resolution tracking of feature-based attentional selection10.1038/s41598-017-01911-02045-2322https://doaj.org/article/10df2ae668ad437a86a518a4533e52be2017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01911-0https://doaj.org/toc/2045-2322Abstract 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.Johannes Jacobus FahrenfortAnna GrubertChristian N. L. OliversMartin EimerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-15 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Johannes Jacobus Fahrenfort Anna Grubert Christian N. L. Olivers Martin Eimer Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection
description	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.
format	article
author	Johannes Jacobus Fahrenfort Anna Grubert Christian N. L. Olivers Martin Eimer
author_facet	Johannes Jacobus Fahrenfort Anna Grubert Christian N. L. Olivers Martin Eimer
author_sort	Johannes Jacobus Fahrenfort
title	Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection
title_short	Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection
title_full	Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection
title_fullStr	Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection
title_full_unstemmed	Multivariate EEG analyses support high-resolution tracking of feature-based attentional selection
title_sort	multivariate eeg analyses support high-resolution tracking of feature-based attentional selection
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/10df2ae668ad437a86a518a4533e52be
work_keys_str_mv	AT johannesjacobusfahrenfort multivariateeeganalysessupporthighresolutiontrackingoffeaturebasedattentionalselection AT annagrubert multivariateeeganalysessupporthighresolutiontrackingoffeaturebasedattentionalselection AT christiannlolivers multivariateeeganalysessupporthighresolutiontrackingoffeaturebasedattentionalselection AT martineimer multivariateeeganalysessupporthighresolutiontrackingoffeaturebasedattentionalselection
_version_	1718388626584240128

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

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