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

ABSTRACT: 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

SUBMITTER: Andreu-Perez J 

PROVIDER: S-EPMC8443619 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature