Search results for "granger causality"

showing 10 items of 81 documents

Critical comments on EEG sensor space dynamical connectivity analysis

2019

Many different analysis techniques have been developed and applied to EEG recordings that allow one to investigate how different brain areas interact. One particular class of methods, based on the linear parametric representation of multiple interacting time series, is widely used to study causal connectivity in the brain. However, the results obtained by these methods should be interpreted with great care. The goal of this paper is to show, both theoretically and using simulations, that results obtained by applying causal connectivity measures on the sensor (scalp) time series do not allow interpretation in terms of interacting brain sources. This is because (1) the channel locations canno…

FOS: Computer and information sciencesComputer scienceSocial SciencesTransfer functionStatistics - Applications050105 experimental psychology03 medical and health sciences0302 clinical medicinegranger causalityMVARHumansApplications (stat.AP)Computer Simulation0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imagingBrain connectivityEEGTime domainSpurious relationshipRepresentation (mathematics)Mixing (physics)Parametric statisticsBrain MappingRadiological and Ultrasound TechnologySeries (mathematics)05 social sciencesbrain connectivitysource modellingElectroencephalographyNeurologyFOS: Biological sciencesFrequency domainQuantitative Biology - Neurons and CognitionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityDirected transfer functionNeurons and Cognition (q-bio.NC)Neurology (clinical)AnatomyAlgorithm030217 neurology & neurosurgery
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Local Granger causality

2021

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. For Gaussian variables it is equivalent to transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes. We exploit such equivalence and calculate exactly the 'local Granger causality', i.e. the profile of the information transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. Our approach offers a robust and computationally fast method to follow the information transfer along the time history of linear stochastic processes, as well as of nonlinear …

FOS: Computer and information sciencesInformation transferGaussianFOS: Physical sciencestechniques; information theory; granger causalityMachine Learning (stat.ML)Quantitative Biology - Quantitative Methods01 natural sciences010305 fluids & plasmasVector autoregressionsymbols.namesakegranger causalityGranger causalityStatistics - Machine Learning0103 physical sciencesApplied mathematicstime serie010306 general physicsQuantitative Methods (q-bio.QM)Mathematicsinformation theoryStochastic processDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksComputational Physics (physics.comp-ph)Discrete time and continuous timeAutoregressive modelFOS: Biological sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticasymbolsTransfer entropytechniquesPhysics - Computational Physics
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Synergetic and redundant information flow detected by unnormalized Granger causality: application to resting state fMRI

2015

Objectives: We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. Methods: The presence of redundancy and/or synergy in multivariate time series data renders difficult to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently we introduce a pairwise index of synergy which is zero when two in…

FOS: Computer and information sciencesgranger causality (GC)Multivariate statisticsComputer scienceRestComputer Science - Information TheoryBiomedical EngineeringsynergyFOS: Physical sciencescomputer.software_genre01 natural sciences03 medical and health sciences0302 clinical medicineGranger causality0103 physical sciencesConnectomeRedundancy (engineering)HumansBrain connectivityTime series010306 general physicsModels StatisticalHuman Connectome ProjectResting state fMRIredundancybusiness.industryInformation Theory (cs.IT)functional magnetic resonance imaging (fMRI)BrainPattern recognitionComplex networkMagnetic Resonance ImagingVariable (computer science)Physics - Data Analysis Statistics and ProbabilityQuantitative Biology - Neurons and CognitionFOS: Biological sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaPairwise comparisonNeurons and Cognition (q-bio.NC)Artificial intelligenceData miningNerve Netbusinesscomputer030217 neurology & neurosurgeryData Analysis Statistics and Probability (physics.data-an)
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A Novel Approach to Propagation Pattern Analysis in Intracardiac Atrial Fibrillation Signals

2010

The purpose of this study is to investigate propagation patterns in intracardiac signals recorded during atrial fibrillation (AF) using an approach based on partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The PDC is evaluated at the dominant frequency of AF signals and tested for significance using a surrogate data procedure specifically designed to assess causality. For significantly coupled sites, the approach allows also to estimate the delay in propagation. The methods potential is illustrated with two simulation scenarios based on a detailed ionic model of the human atrial myocyte as well as with real data recordi…

Frequency analysiComputer scienceBiomedical EngineeringElectrogramAction PotentialsIntracardiac injectionPattern Recognition AutomatedSurrogate datalaw.inventionHeart Conduction SystemlawAtrial FibrillationmedicineHumansCoherence (signal processing)Computer SimulationDiagnosis Computer-AssistedSimulationFrequency analysisbusiness.industryBody Surface Potential MappingPartial directed coherenceModels CardiovascularPropagation patternAtrial fibrillationPattern recognitionAtrial arrhythmiamedicine.diseaseInformation engineeringMappingFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityMultivariate autoregressive modelingArtificial intelligencebusinessSimulationAnnals of Biomedical Engineering
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The Nexus between Sovereign CDS and Stock Market Volatility: New Evidence

2021

This paper extends the studies published to date by performing an analysis of the causal relationships between sovereign CDS spreads and the estimated conditional volatility of stock indices. This estimation is performed using a vector autoregressive model (VAR) and dynamically applying the Granger causality test. The conditional volatility of the stock market has been obtained through various univariate GARCH models. This methodology allows us to study the information transmissions, both unidirectional and bidirectional, that occur between CDS spreads and stock volatility between 2004 and 2020. We conclude that CDS spread returns cause (in the Granger sense) conditional stock volatility, m…

GARCHGeneral MathematicsAutoregressive conditional heteroskedasticitycds sovereign spread:CIENCIAS ECONÓMICAS [UNESCO]granger causalityGranger causalitygarch0502 economics and businessComputer Science (miscellaneous)EconomicsEconometricsQA1-939050207 economicsvarEngineering (miscellaneous)Stock (geology)050208 financeCDS sovereign spread05 social sciencesUnivariateUNESCO::CIENCIAS ECONÓMICASStock market indexconditional volatilityAutoregressive modelGranger causalityStock marketVARVolatility (finance)MathematicsMathematics
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A framework for assessing frequency domain causality in physiological time series with instantaneous effects.

2013

We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the kno…

General MathematicsGeneral Physics and AstronomyModels BiologicalCausality (physics)Physics and Astronomy (all)Engineering (all)Granger causalityEconometricsMathematics (all)Coherence (signal processing)AnimalsHumansComputer SimulationDirected coherenceMathematicsMultivariate autoregressive modelModels StatisticalSeries (mathematics)Partial directed coherenceGeneral EngineeringSystem identificationAC powerAutoregressive modelFrequency domainSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityDirected coherence; Granger causality; Multivariate autoregressive models; Partial directed coherence; Mathematics (all); Engineering (all); Physics and Astronomy (all)AlgorithmsPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
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Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate va…

2021

Assessing Granger causality (GC) intended as the influence, in terms of reduction of variance of surprise, that a driver variable exerts on a given target, requires a suitable treatment of ‘instantaneous’ effects, i.e. influences due to interactions whose time scale is much faster than the time resolution of the measurements, due to unobserved confounders or insufficient sampling rate that cannot be increased because the mechanism of generation of the variable is inherently slow (e.g. the heartbeat). We exploit a recently proposed framework for the estimation of causal influences in the spectral domain and include instantaneous interactions in the modelling, thus obtaining (i) a novel index…

General MathematicsGeneral Physics and AstronomyVector autoregressionMatrix decompositionCausality (physics)granger causalityGranger causalityHeart RateEconometricsvector autoregressionMedicine and Health SciencesHeart rate variabilitycardiorespiratory systemComputer SimulationTime seriesMathematicsinformation theoryGeneral Engineeringheart rate variabilityVariance (accounting)BaroreflexScience Generalspectral analysisCausalityVariable (computer science)Mathematics and Statisticstime series analysisAlgorithmsPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
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Centres and peripheries in Finland: Granger causality tests using panel data

2009

Abstract Despite their importance from a policy point of view, empirical studies on the effects of growth centres in their regions are rare. This paper analyses mutual relationships between growth processes in centres and their surrounding hinterlands in 19 Finnish regions. Annual population data from the period 1970–2004 are used. A novel testing procedure based on an extension of the Granger causality definition in a panel data context is applied. Heterogeneity between regions is allowed. Both the homogeneous non-causality hypothesis and the homogeneous causality hypothesis are rejected. Causal processes prove to be heterogeneous. Causality from centres to peripheries is found for nine re…

Geography Planning and DevelopmentContext (language use)keskus-periferiaCore peripheryGranger kausaalisuusCausalityEmpirical researchGeographyGranger causalityHomogeneousEarth and Planetary Sciences (miscellaneous)EconometricsPopulation dataGranger causalitycore-peripheryEconomic geographyStatistics Probability and Uncertaintyregional growthaluekasvuGeneral Economics Econometrics and Financekasvukeskusgrowth centrePanel data
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Granger causality analysis of sleep brain-heart interactions

2014

We studied the networks of Granger causality (GC) between the time series of cardiac vagal autonomic activity and brain wave activities, measured respectively as the normalized high frequency (HF) component of heart rate variability and EEG power in the δ, θ, α, σ, β bands, computed in 10 healthy subjects during sleep. GC analysis was performed by vector autoregressive modeling, and significance of each link in the network was assessed using F-statistics. The whole-night analysis revealed the existence of a fully connected network of brain-heart and brain-brain interactions, with the ß EEG power acting as a hub which conveys the largest number of GC links between the heart and brain n…

Granger causality analysismedicine.diagnostic_testBiomedical EngineeringHealthy subjectsElectroencephalographySleep in non-human animalsGranger causalityAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticamedicineHeart rate variabilityPsychologyNeuroscienceSlow-wave sleep2014 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
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Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

2016

The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amou…

Information transferDynamical systems theoryComputer scienceGeneral Physics and Astronomylcsh:AstrophysicsInformation theorycomputer.software_genreMachine learning01 natural sciencesEntropy - Cardiorespiratory interactions - Dynamical systems -cardiovascular interactions03 medical and health sciencessymbols.namesake0302 clinical medicinelcsh:QB460-4660103 physical sciencesinformation transferEntropy (information theory)lcsh:Science010306 general physicsGaussian processautoregressive processesmultivariate time series analysisbusiness.industryautonomic nervous systemredundancy and synergycardiorespiratory interactionsdynamical systemsComplex networkNetwork dynamicslcsh:QC1-999autonomic nervous system; autoregressive processes; cardiorespiratory interactions; cardiovascular interactions; Granger causality; dynamical systems; information dynamics; information transfer; redundancy and synergy; multivariate time series analysisAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalitysymbolslcsh:QArtificial intelligenceData mininginformation dynamicsbusinesscomputerlcsh:Physics030217 neurology & neurosurgeryEntropy; Volume 19; Issue 1; Pages: 5
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