Group level characterization of brain networks still
represents an open issue in modern neuroscience. Investigating
the functional mechanisms underlying the complexity of the
human brain requires an analytical way to efficiently integrate
information from multiple subjects, while properly handling the
intrinsic inter-subject variability. Here we investigated the
potentiality of the PARAllel FACtorization (PARAFAC)
algorithm for the extraction of grand average brain connectivity
matrices from simulated EEG datasets. Synthetic data were
parametrized according to different levels of three parameters:
network dimension (NODES), number of observations (SAMPSIZE)
and noise (SWAP-CON) in order to investigate the way
they affect the reconstruction of grand average networks.
Robustness to noise as well as the proper modulation of
informative content have here been proved empirically, revealing
that the best performances in terms of FPR, FNR and AUC were
achieved for great number of observations and low noise level.
Dettaglio pubblicazione
2023, VIII Congress of the National Group of Bioengineering (GNB), Pages -
On the use of PARAFAC algorithm in group network analysis: a simulation study (04b Atto di convegno in volume)
Ranieri A., Pichiorri F., Colamarino E., de Seta V., Mattia D., Toppi J.
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