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Explainable Artificial Intelligence Based Analysis for Developmental Cognitive Neuroscience

Andreu-Perez, Javier and Emberson, Lauren L and Kiani, Mehrin and Filippetti, Maria Laura and Hagras, Hani and Rigato, Silvia (2021) 'Explainable Artificial Intelligence Based Analysis for Developmental Cognitive Neuroscience.' Communications Biology, 4. ISSN 2399-3642

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In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Science and Health > Psychology, Department of
Depositing User: Elements
Date Deposited: 22 Sep 2021 12:51
Last Modified: 22 Sep 2021 13:15

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