SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2021 Jun 2

PMID 34071124

Citations 7

Authors

Alessandra Anzolin

Jlenia Toppi

Manuela Petti

Febo Cincotti

Laura Astolfi

Affiliations

Soon will be listed here.

Abstract

EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user's needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.

Citing Articles

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