A regularized and smoothed General Linear Kalman Filter for more accurate estimation of time-varying directed connectivity*
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[1] Rudolph van der Merwe,et al. The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).
[2] C. Granger. Investigating causal relations by econometric models and cross-spectral methods , 1969 .
[3] Lester Melie-García,et al. Estimating brain functional connectivity with sparse multivariate autoregression , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.
[4] Mingzhou Ding,et al. Analyzing information flow in brain networks with nonparametric Granger causality , 2008, NeuroImage.
[5] H. Sompolinsky,et al. Sparseness and Expansion in Sensory Representations , 2014, Neuron.
[6] Daoqiang Zhang,et al. Constrained Sparse Functional Connectivity Networks for MCI Classification , 2012, MICCAI.
[7] Olaf Sporns,et al. The small world of the cerebral cortex , 2007, Neuroinformatics.
[8] Bin He,et al. Estimation of Time-Varying Connectivity Patterns Through the Use of an Adaptive Directed Transfer Function , 2008, IEEE Transactions on Biomedical Engineering.
[9] Dezhong Yao,et al. Lp (p ≤ 1) Norm Partial Directed Coherence for Directed Network Analysis of Scalp EEGs , 2018, Brain Topography.
[10] Alexandre Andrade,et al. Synthetic neuronal datasets for benchmarking directed functional connectivity metrics , 2015, PeerJ.
[11] Stefan Haufe,et al. Sparse Causal Discovery in Multivariate Time Series , 2008, NIPS Causality: Objectives and Assessment.
[12] P Girard,et al. Feedback connections act on the early part of the responses in monkey visual cortex. , 2001, Journal of neurophysiology.
[13] Dan Simon,et al. Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .
[14] R. Tibshirani,et al. Regression shrinkage and selection via the lasso: a retrospective , 2011 .
[15] Michael A. West,et al. Covariance decomposition in undirected Gaussian graphical models , 2005 .
[16] Henry Kennedy,et al. Cortical High-Density Counterstream Architectures , 2013, Science.
[17] C. Striebel,et al. On the maximum likelihood estimates for linear dynamic systems , 1965 .
[18] Laura Astolfi,et al. Early recurrence and ongoing parietal driving during elementary visual processing , 2015, Scientific Reports.
[19] Stefan Haufe,et al. A critical assessment of connectivity measures for EEG data: A simulation study , 2013, NeuroImage.
[20] Christopher J. Rozell,et al. Dynamic Filtering of Time-Varying Sparse Signals via $\ell _1$ Minimization , 2015, IEEE Transactions on Signal Processing.
[21] Mukesh Dhamala,et al. Benchmarking nonparametric Granger causality: Robustness against downsampling and influence of spectral decomposition parameters , 2018, NeuroImage.
[22] Patrick Dupont,et al. A Time-Varying Connectivity Analysis from Distributed EEG Sources: A Simulation Study , 2018, Brain Topography.
[23] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[24] Mattia F. Pagnotta,et al. Time-varying MVAR algorithms for directed connectivity analysis: Critical comparison in simulations and benchmark EEG data , 2018, PloS one.
[25] Danielle Smith Bassett,et al. Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[26] Laura Astolfi,et al. Towards the time varying estimation of complex brain connectivity networks by means of a General Linear Kalman Filter approach , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[27] M. Arnold,et al. Dynamic cross-spectral analysis of biological signals by means of bivariate ARMA processes with time-dependent coefficients , 1995, Medical and Biological Engineering and Computing.
[28] A. Wennberg,et al. Computer analysis of EEG signals with parametric models , 1981, Proceedings of the IEEE.
[29] Hassan M. Fathallah-Shaykh,et al. Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother , 2014, EURASIP J. Bioinform. Syst. Biol..
[30] Jeffrey K. Uhlmann,et al. New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.
[31] Stephen P. Boyd,et al. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..
[32] O. Sporns,et al. Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.
[33] Alexis Hervais-Adelman,et al. Gating by induced α-γ asynchrony in selective attention , 2017, bioRxiv.
[34] Luiz A. Baccalá,et al. Information theoretic interpretation of frequency domain connectivity measures , 2010, Biological Cybernetics.
[35] Mark W. Woolrich,et al. Task-Evoked Dynamic Network Analysis Through Hidden Markov Modeling , 2018, Front. Neurosci..