ArticleSensors (Basel, Switzerland)2021
SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An evaluation of inter-brain EEG coupling methods in hyperscanning studies.Cognitive neurodynamics · 2024Article
- Temporal segmentation of EEG based on functional connectivity network structure.Scientific reports · 2023Article
- Identifying good practices for detecting inter-regional linear functional connectivity from EEG.NeuroImage · 2023Article
- Independent evaluation of the harvard automated processing pipeline for Electroencephalography 1.0 using multi-site EEG data from children with Fragile X Syndrome.Journal of neuroscience methods · 2022Article
- Complex Pearson Correlation Coefficient for EEG Connectivity Analysis.Sensors (Basel, Switzerland) · 2022Article
- Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2022Review
- Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.