Brain fingerprinting using EEG graph inference
Miri, M.; Abootalebi, V.; Amico, E.; Saeedi-Sourck, H.; Van De Ville, D.; Behjat, H.
Show 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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