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A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations
2019
Entropy
High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally,
doi:10.3390/e21070629
pmid:33267342
fatcat:fmeoep7kcrc7pkzrmhxmiboe6y