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

Javier Andreu-Perez; Lauren L. Emberson; Mehrin Kiani; Maria Laura Filippetti; Hani Hagras; Silvia Rigato

doi:10.1038/s42003-021-02534-y

Communications Biology (Sep 2021)

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

Javier Andreu-Perez,
Lauren L. Emberson,
Mehrin Kiani,
Maria Laura Filippetti,
Hani Hagras,
Silvia Rigato

Affiliations

Javier Andreu-Perez: Centre for Computational Intelligence, University of Essex
Lauren L. Emberson: Department of Psychology, Princeton University
Mehrin Kiani: Centre for Computational Intelligence, University of Essex
Maria Laura Filippetti: Centre for Brain Science, Department of Psychology, University of Essex
Hani Hagras: Centre for Computational Intelligence, University of Essex
Silvia Rigato: Centre for Brain Science, Department of Psychology, University of Essex

DOI: https://doi.org/10.1038/s42003-021-02534-y
Journal volume & issue: Vol. 4, no. 1
pp. 1 – 13

Abstract

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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.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

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