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Brain fingerprinting using EEG graph inference

Miri, Maliheh LU ; Abootalebi, Vahid ; Amico, Enrico ; Saeedi-Sourck, Hamid ; Van De Ville, Dimitri and Behjat, Hamid LU (2023) 31st European Signal Processing Conference, EUSIPCO 2023 In European Signal Processing Conference p.1025-1029
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.

Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Brain Functional Connectivity, EEG, Fingerprinting, Graph Learning, Graph Signal Processing
host publication
31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
series title
European Signal Processing Conference
pages
5 pages
publisher
European Signal Processing Conference, EUSIPCO
conference name
31st European Signal Processing Conference, EUSIPCO 2023
conference location
Helsinki, Finland
conference dates
2023-09-04 - 2023-09-08
external identifiers
  • scopus:85178378453
ISSN
2219-5491
ISBN
9789464593600
DOI
10.23919/EUSIPCO58844.2023.10289864
language
English
LU publication?
yes
id
bc2357c7-61cf-4d9a-ab64-67c96c3462e5
date added to LUP
2024-01-02 15:20:41
date last changed
2024-01-02 15:22:53
@inproceedings{bc2357c7-61cf-4d9a-ab64-67c96c3462e5,
  abstract     = {{<p>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.</p>}},
  author       = {{Miri, Maliheh and Abootalebi, Vahid and Amico, Enrico and Saeedi-Sourck, Hamid and Van De Ville, Dimitri and Behjat, Hamid}},
  booktitle    = {{31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings}},
  isbn         = {{9789464593600}},
  issn         = {{2219-5491}},
  keywords     = {{Brain Functional Connectivity; EEG; Fingerprinting; Graph Learning; Graph Signal Processing}},
  language     = {{eng}},
  pages        = {{1025--1029}},
  publisher    = {{European Signal Processing Conference, EUSIPCO}},
  series       = {{European Signal Processing Conference}},
  title        = {{Brain fingerprinting using EEG graph inference}},
  url          = {{http://dx.doi.org/10.23919/EUSIPCO58844.2023.10289864}},
  doi          = {{10.23919/EUSIPCO58844.2023.10289864}},
  year         = {{2023}},
}