Cargando…

A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification

The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG) study, we used single-trial classification to perform a Representational Similarity Analysis (RSA) of categorical represe...

Descripción completa

Detalles Bibliográficos
Autores principales: Kaneshiro, Blair, Perreau Guimaraes, Marcos, Kim, Hyung-Suk, Norcia, Anthony M., Suppes, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4546653/
https://www.ncbi.nlm.nih.gov/pubmed/26295970
http://dx.doi.org/10.1371/journal.pone.0135697
_version_ 1782386963357106176
author Kaneshiro, Blair
Perreau Guimaraes, Marcos
Kim, Hyung-Suk
Norcia, Anthony M.
Suppes, Patrick
author_facet Kaneshiro, Blair
Perreau Guimaraes, Marcos
Kim, Hyung-Suk
Norcia, Anthony M.
Suppes, Patrick
author_sort Kaneshiro, Blair
collection PubMed
description The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG) study, we used single-trial classification to perform a Representational Similarity Analysis (RSA) of categorical representation of objects in human visual cortex. Brain responses were recorded while participants viewed a set of 72 photographs of objects with a planned category structure. The Representational Dissimilarity Matrix (RDM) used for RSA was derived from confusions of a linear classifier operating on single EEG trials. In contrast to past studies, which used pairwise correlation or classification to derive the RDM, we used confusion matrices from multi-class classifications, which provided novel self-similarity measures that were used to derive the overall size of the representational space. We additionally performed classifications on subsets of the brain response in order to identify spatial and temporal EEG components that best discriminated object categories and exemplars. Results from category-level classifications revealed that brain responses to images of human faces formed the most distinct category, while responses to images from the two inanimate categories formed a single category cluster. Exemplar-level classifications produced a broadly similar category structure, as well as sub-clusters corresponding to natural language categories. Spatiotemporal components of the brain response that differentiated exemplars within a category were found to differ from those implicated in differentiating between categories. Our results show that a classification approach can be successfully applied to single-trial scalp-recorded EEG to recover fine-grained object category structure, as well as to identify interpretable spatiotemporal components underlying object processing. Finally, object category can be decoded from purely temporal information recorded at single electrodes.
format Online
Article
Text
id pubmed-4546653
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-45466532015-09-01 A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification Kaneshiro, Blair Perreau Guimaraes, Marcos Kim, Hyung-Suk Norcia, Anthony M. Suppes, Patrick PLoS One Research Article The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG) study, we used single-trial classification to perform a Representational Similarity Analysis (RSA) of categorical representation of objects in human visual cortex. Brain responses were recorded while participants viewed a set of 72 photographs of objects with a planned category structure. The Representational Dissimilarity Matrix (RDM) used for RSA was derived from confusions of a linear classifier operating on single EEG trials. In contrast to past studies, which used pairwise correlation or classification to derive the RDM, we used confusion matrices from multi-class classifications, which provided novel self-similarity measures that were used to derive the overall size of the representational space. We additionally performed classifications on subsets of the brain response in order to identify spatial and temporal EEG components that best discriminated object categories and exemplars. Results from category-level classifications revealed that brain responses to images of human faces formed the most distinct category, while responses to images from the two inanimate categories formed a single category cluster. Exemplar-level classifications produced a broadly similar category structure, as well as sub-clusters corresponding to natural language categories. Spatiotemporal components of the brain response that differentiated exemplars within a category were found to differ from those implicated in differentiating between categories. Our results show that a classification approach can be successfully applied to single-trial scalp-recorded EEG to recover fine-grained object category structure, as well as to identify interpretable spatiotemporal components underlying object processing. Finally, object category can be decoded from purely temporal information recorded at single electrodes. Public Library of Science 2015-08-21 /pmc/articles/PMC4546653/ /pubmed/26295970 http://dx.doi.org/10.1371/journal.pone.0135697 Text en © 2015 Kaneshiro et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Kaneshiro, Blair
Perreau Guimaraes, Marcos
Kim, Hyung-Suk
Norcia, Anthony M.
Suppes, Patrick
A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification
title A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification
title_full A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification
title_fullStr A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification
title_full_unstemmed A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification
title_short A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification
title_sort representational similarity analysis of the dynamics of object processing using single-trial eeg classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4546653/
https://www.ncbi.nlm.nih.gov/pubmed/26295970
http://dx.doi.org/10.1371/journal.pone.0135697
work_keys_str_mv AT kaneshiroblair arepresentationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT perreauguimaraesmarcos arepresentationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT kimhyungsuk arepresentationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT norciaanthonym arepresentationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT suppespatrick arepresentationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT kaneshiroblair representationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT perreauguimaraesmarcos representationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT kimhyungsuk representationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT norciaanthonym representationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification
AT suppespatrick representationalsimilarityanalysisofthedynamicsofobjectprocessingusingsingletrialeegclassification