0000000000133745

AUTHOR

Jlenia Toppi

showing 5 related works from this author

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Artificial neural networks; Chaotic oscillators; Granger causality; Multivariate time series analysis; Network physiology; Penalized regression techniques; Remote synchronization; State-space models; Stochastic gradient descent L1; Vector autoregressive modelGeneral Computer ScienceDynamical systems theoryComputer science02 engineering and technologyChaotic oscillatorsPenalized regression techniquesNetwork topologySettore ING-INF/01 - ElettronicaMultivariate time series analysisVector autoregression03 medical and health sciences0302 clinical medicineScientific Computing and Simulation0202 electrical engineering electronic engineering information engineeringRepresentation (mathematics)Optimization Theory and ComputationNetwork physiologyState-space modelsArtificial neural networkArtificial neural networksData ScienceTheory and Formal MethodsQA75.5-76.95Stochastic gradient descent L1Granger causality State-space models Vector autoregressive model Artificial neural networks Stochastic gradient descent L1 Multivariate time series analysis Network physiology Remote synchronization Chaotic oscillators Penalized regression techniquesRemote synchronizationStochastic gradient descentAutoregressive modelAlgorithms and Analysis of AlgorithmsVector autoregressive modelElectronic computers. Computer scienceSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causality020201 artificial intelligence & image processingGradient descentAlgorithm030217 neurology & neurosurgeryPeerJ Computer Science
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Estimating brain connectivity when few data points are available: Perspectives and limitations

2017

Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in …

Multivariate statisticsUnderdetermined system0206 medical engineeringBiomedical EngineeringSignal Processing; Biomedical Engineering; 1707; Health InformaticsHealth Informatics02 engineering and technologyMachine learningcomputer.software_genreBrain Mapping Brain03 medical and health sciences0302 clinical medicineFalse positive paradox1707MathematicsBrain Mappingbusiness.industryBrain020601 biomedical engineeringRegressionData pointAutoregressive modelMulticollinearitySignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaOrdinary least squaresArtificial intelligenceData miningbusinesscomputer030217 neurology & neurosurgery2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Estimation of brain connectivity through Artificial Neural Networks

2019

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points. The mathematical solution to these problems is represented by penalized regression methods based on l 1 norm, that can reduce collinearity by means of variable sel…

Computer scienceFeature selection02 engineering and technologyConnectivity measurements03 medical and health sciences0302 clinical medicine0202 electrical engineering electronic engineering information engineeringArtificial neural networkbusiness.industryProcess (computing)BrainPattern recognitionElectroencephalographyCollinearityCausalityData pointCausality; Connectivity measurements; Physiological systems modeling - Multivariate signal processingNorm (mathematics)Physiological systems modeling - Multivariate signal processingRegression Analysis020201 artificial intelligence & image processingAnalysis of varianceArtificial intelligenceNeural Networks ComputerbusinessAlgorithms Brain Electroencephalography Regression Analysis Neural Networks Computer030217 neurology & neurosurgeryLinear equationAlgorithms
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Measuring the agreement between brain connectivity networks.

2016

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing th…

Computer scienceModels NeurologicalStructure (category theory)Biomedical EngineeringSignal Processing; Biomedical Engineering; 1707; Health InformaticsHealth Informatics02 engineering and technologycomputer.software_genreMeasure (mathematics)Surrogate dataData modeling03 medical and health sciencesAnalysis of Variance Area Under Curve Brain Brain Mapping Computer Simulation Electroencephalography Humans Nerve Net Signal Processing Computer-Assisted Models Neurological0302 clinical medicineSimilarity (network science)0202 electrical engineering electronic engineering information engineeringHumansComputer SimulationSensitivity (control systems)1707Analysis of VarianceBrain MappingBrainElectroencephalographySignal Processing Computer-AssistedArea Under CurveSignal Processing020201 artificial intelligence & image processingData miningNerve Netcomputer030217 neurology & neurosurgeryAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator.

2019

Methods based on the use of multivariate autoregressive models (MVAR) have proved to be an accurate tool for the estimation of functional links between the activity originated in different brain regions. A well-established method for the parameters estimation is the Ordinary Least Square (OLS) approach, followed by an assessment procedure that can be performed by means of Asymptotic Statistic (AS). However, the performances of both procedures are strongly influenced by the number of data samples available, thus limiting the conditions in which brain connectivity can be estimated. The aim of this paper is to introduce and test a regression method based on Least Absolute Shrinkage and Selecti…

Multivariate statisticsComputer science0206 medical engineering02 engineering and technologyConnectivity measurementsLeast squares03 medical and health sciences0302 clinical medicineLasso (statistics)Statistics::MethodologyLeast-Squares AnalysisStatisticShrinkagebusiness.industryBrainPattern recognitionElectroencephalography020601 biomedical engineeringCausalityData pointAutoregressive modelCausality; Connectivity measurements; Physiological systems modeling - Multivariate signal processingPhysiological systems modeling - Multivariate signal processingOrdinary least squaresLeast-Squares Analysis Brain ElectroencephalographyArtificial intelligencebusiness030217 neurology & neurosurgeryAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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