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bioRxiv · 10.1101/2023.03.11.532201

Brain fingerprinting using EEG graph inference

Miri, M.·Abootalebi, V.·Amico, E.·Saeedi-Sourck, H.·Van De Ville, D.·Behjat, H.
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Abstract

Taking advantage of the human brain functional connectome as an individuals fingerprint has attracted great research in recent years. Conventionally, Pearson correlation between regional time-courses is used as a pairwise measure for each edge weight of the connectome. Building upon recent advances in graph signal processing, we propose here to estimate the graph structure as a whole by considering all time-courses at once. Using data from two publicly available datasets, we show the superior performance of such learned brain graphs over correlation-based functional connectomes in characterizing an individual.

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Miri, M., Abootalebi, V., Amico, E., Saeedi-Sourck, H., Van De Ville, D., Behjat, H.. 2023-03-12. Brain fingerprinting using EEG graph inference. https://doi.org/10.1101/2023.03.11.532201

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