Seed-g: Simulated eeg data generator for testing connectivity algorithms

Alessandra Anzolin, Jlenia Toppi, Manuela Petti, Febo Cincotti, Laura Astolfi

Research output: Contribution to journalArticlepeer-review

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, confirm-ing its robustness.

Original languageEnglish
Article number3632
JournalSensors
Volume21
Issue number11
DOIs
Publication statusPublished - Jun 1 2021

Keywords

  • Brain connectivity
  • EEG
  • Ground-truth networks
  • Multivariate autoregressive models
  • Partial directed coherence
  • Simulated neuro-electrical data

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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