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dc.contributor.authorHu, Lechuan
dc.contributor.authorFortin, Norbert
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2017-12-28T07:32:14Z
dc.date.available2017-12-28T07:32:14Z
dc.date.issued2017-03-27
dc.identifier.urihttp://hdl.handle.net/10754/626516
dc.description.abstractIn this paper, our goal is to model functional and effective (directional) connectivity in network of multichannel brain physiological signals (e.g., electroencephalograms, local field potentials). The primary challenges here are twofold: first, there are major statistical and computational difficulties for modeling and analyzing high dimensional multichannel brain signals; second, there is no set of universally-agreed measures for characterizing connectivity. To model multichannel brain signals, our approach is to fit a vector autoregressive (VAR) model with sufficiently high order so that complex lead-lag temporal dynamics between the channels can be accurately characterized. However, such a model contains a large number of parameters. Thus, we will estimate the high dimensional VAR parameter space by our proposed hybrid LASSLE method (LASSO+LSE) which is imposes regularization on the first step (to control for sparsity) and constrained least squares estimation on the second step (to improve bias and mean-squared error of the estimator). Then to characterize connectivity between channels in a brain network, we will use various measures but put an emphasis on partial directed coherence (PDC) in order to capture directional connectivity between channels. PDC is a directed frequency-specific measure that explains the extent to which the present oscillatory activity in a sender channel influences the future oscillatory activity in a specific receiver channel relative all possible receivers in the network. Using the proposed modeling approach, we have achieved some insights on learning in a rat engaged in a non-spatial memory task.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1703.08920v1
dc.relation.urlhttp://arxiv.org/pdf/1703.08920v1
dc.rightsArchived with thanks to arXiv
dc.titleModeling high dimensional multichannel brain signals
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Statistics, University of California, Irvine, USA
dc.contributor.institutionDepartment of Neurobiology and Behavior, University of California, Irvine, USA
dc.identifier.arxividarXiv:1703.08920
kaust.personOmbao, Hernando
refterms.dateFOA2018-06-13T12:43:48Z


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