Brain fingerprinting using EEG graph inference
Miri, Maliheh; Abootalebi, Vahid; Amico, Enrico; Saeedi-Sourck, Hamid, et al. (2023). Brain fingerprinting using EEG graph inference 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings, 1025 - 1029. 31st European Signal Processing Conference, EUSIPCO 2023. Helsinki, Finland: European Signal Processing Conference, EUSIPCO
Conference Proceeding/Paper
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Published
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English
Authors:
Miri, Maliheh
;
Abootalebi, Vahid
;
Amico, Enrico
;
Saeedi-Sourck, Hamid
, et al.
Department:
Department of Biomedical Engineering
LU Profile Area: Proactive Ageing
MultiPark: Multidisciplinary research focused on ParkinsonĀ“s disease
Clinical Memory Research
Research Group:
Clinical Memory Research
Abstract:
Taking advantage of the human brain functional connectome as an individual's 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.
Keywords:
Brain Functional Connectivity ;
EEG ;
Fingerprinting ;
Graph Learning ;
Graph Signal Processing
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