Brain fingerprinting using EEG graph inference
(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:
https://lup.lub.lu.se/record/bc2357c7-61cf-4d9a-ab64-67c96c3462e5
- author
- Miri, Maliheh LU ; Abootalebi, Vahid ; Amico, Enrico ; Saeedi-Sourck, Hamid ; Van De Ville, Dimitri and Behjat, Hamid LU
- organization
- publishing date
- 2023
- 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}}, }