European Signal Processing Conference (EUSIPCO)NeurosciencePeer-reviewed journal article
Brain fingerprinting using EEG graph inference
Maliheh MiriiD, Vahid AbootalebiiD, Enrico AmicoiD, Hamid Saeedi‐SourckiD, Dimitri Van De VilleiD, Hamid BehjatiD
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.
- organism: human
- assay: electronic health records
- research focus: dimensionality reduction
- approach: graph neural network
- Citations
- 5
- FWCI
- 2.57
- Field percentile
- top 11%
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