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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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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 |
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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 |
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