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

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 p

SUBMITTER: Anzolin A 

PROVIDER: S-EPMC8197139 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature