Brain fingerprinting using EEG graph inference

Maliheh Miri; Vahid Abootalebi; Enrico Amico; Hamid Saeedi-Sourck; Dimitri Van De Ville; Hamid Behjat

doi:10.23919/EUSIPCO58844.2023.10289864

Brain fingerprinting using EEG graph inference

Maliheh Miri, Vahid Abootalebi, Enrico Amico, Hamid Saeedi-Sourck, Dimitri Van De Ville, Hamid Behjat^*

^*Corresponding author for this work

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	2023 31st European Signal Processing Conference (EUSIPCO)
Publisher	IEEE
Pages	1025-1029
Number of pages	5
ISBN (Electronic)	9789464593600
ISBN (Print)	9798350328110 (PoD)
DOIs	https://doi.org/10.23919/EUSIPCO58844.2023.10289864
Publication status	Published - 1 Nov 2023
Event	31st European Signal Processing Conference, EUSIPCO 2023 - Helsinki, Finland Duration: 4 Sept 2023 → 8 Sept 2023

Publication series

Name	European Signal Processing Conference
Publisher	IEEE
ISSN (Print)	2219-5491
ISSN (Electronic)	2076-1465

Conference

Conference	31st European Signal Processing Conference, EUSIPCO 2023
Country/Territory	Finland
City	Helsinki
Period	4/09/23 → 8/09/23

Bibliographical note

Publisher Copyright:
© 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

Keywords

Brain Functional Connectivity
EEG
Fingerprinting
Graph Learning
Graph Signal Processing

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering

Access to Document

10.23919/EUSIPCO58844.2023.10289864Licence: None: All rights reserved

1 Preprint

Brain fingerprinting using EEG graph inference
Miri, M., Abootalebi, V., Amico, E., Saeedi-Sourck, H., Van De Ville, D. & Behjat, H., 18 Jun 2023, bioRxiv.
Research output: Working paper/Preprint › Preprint

Cite this

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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.",

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Miri, M, Abootalebi, V, Amico, E, Saeedi-Sourck, H, Van De Ville, D & Behjat, H 2023, Brain fingerprinting using EEG graph inference. in 2023 31st European Signal Processing Conference (EUSIPCO). European Signal Processing Conference, IEEE, pp. 1025-1029, 31st European Signal Processing Conference, EUSIPCO 2023, Helsinki, Finland, 4/09/23. https://doi.org/10.23919/EUSIPCO58844.2023.10289864

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AU - Miri, Maliheh

AU - Abootalebi, Vahid

AU - Amico, Enrico

AU - Saeedi-Sourck, Hamid

AU - Van De Ville, Dimitri

AU - Behjat, Hamid

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AB - 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.

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KW - EEG

KW - Fingerprinting

KW - Graph Learning

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