Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

Andreu-Perez et al developed a multivariate pattern analysis for fNIRS data (xMVPA), which is powered by eXplainable Artificial Intelligence (XAI). They demonstrated its application in the context of investigating visual and auditory processing in six-month-old infants and showed that it provided in...

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Autores principales:	Javier Andreu-Perez, Lauren L. Emberson, Mehrin Kiani, Maria Laura Filippetti, Hani Hagras, Silvia Rigato
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/92e7311fbe7145d8a12d8903b24cf3ed
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spelling	oai:doaj.org-article:92e7311fbe7145d8a12d8903b24cf3ed2021-12-02T18:34:00ZExplainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience10.1038/s42003-021-02534-y2399-3642https://doaj.org/article/92e7311fbe7145d8a12d8903b24cf3ed2021-09-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02534-yhttps://doaj.org/toc/2399-3642Andreu-Perez et al developed a multivariate pattern analysis for fNIRS data (xMVPA), which is powered by eXplainable Artificial Intelligence (XAI). They demonstrated its application in the context of investigating visual and auditory processing in six-month-old infants and showed that it provided insight into patterns of cortical networks.Javier Andreu-PerezLauren L. EmbersonMehrin KianiMaria Laura FilippettiHani HagrasSilvia RigatoNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Javier Andreu-Perez Lauren L. Emberson Mehrin Kiani Maria Laura Filippetti Hani Hagras Silvia Rigato Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
description	Andreu-Perez et al developed a multivariate pattern analysis for fNIRS data (xMVPA), which is powered by eXplainable Artificial Intelligence (XAI). They demonstrated its application in the context of investigating visual and auditory processing in six-month-old infants and showed that it provided insight into patterns of cortical networks.
format	article
author	Javier Andreu-Perez Lauren L. Emberson Mehrin Kiani Maria Laura Filippetti Hani Hagras Silvia Rigato
author_facet	Javier Andreu-Perez Lauren L. Emberson Mehrin Kiani Maria Laura Filippetti Hani Hagras Silvia Rigato
author_sort	Javier Andreu-Perez
title	Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
title_short	Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
title_full	Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
title_fullStr	Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
title_full_unstemmed	Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
title_sort	explainable artificial intelligence based analysis for interpreting infant fnirs data in developmental cognitive neuroscience
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/92e7311fbe7145d8a12d8903b24cf3ed
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Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience

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