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

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

doi:10.3390/s21113632

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.

Highlights

We found that the computational time and the Extra Required Iterations (ERIt) increased with the model size and the network density
We decided to present the trend of the relative error computed only for 10-node models (Figure 5) and to provide codes and data to replicate them for the other experimental conditions
We found that the estimation error associated with the Partial Directed Coherence (PDC) computation was significantly higher when the number of modified trials (TRIALS) increases as well as the amount of modified connections (MOD_CON) in both positive (Figure 5a) and negative (Figure 5b) amplitude variations

Summary

Introduction

Connectivity estimators are powerful tools to determine which brain areas are mainly involved in the execution of motor and cognitive tasks, and how they communicate to generate the complex brain networks underlying specific cerebral functions. Several connectivity estimators with different features have been developed to assess the existence, the intensity and the direction of the statistical connections linking two or more time series. New algorithms and adaptations of existing ones should always be validated and compared to previous methods in order to define guidelines for their best use. In both cases, the quality assessment of the connectivity estimates requires time series with a known underlying connectivity pattern (ground-truth network), which can be used as a test bench

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