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080102s2008 caua sb 000 0 eng d |
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|a 1598295330 (electronic bk.)
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|a 9781598295337 (electronic bk.)
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|a 1598295322 (pbk.)
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|a 9781598295337 (pbk.)
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|a 10.2200/S00094ED1V01Y200708BME013
|2 doi
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|a (CaBNvSL)gtp00531397
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|a WAU
|c WAU
|d CaBNvSL
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|a QP383.15
|b .A884 2008
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|a 612.8/25
|2 22
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|a Astolfi, Laura.
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|a Estimation of cortical connectivity in humans
|b advanced signal processing techniques /
|c Laura Astolfi and Fabio Babiloni.
|h [electronic resource] :
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|a San Rafael, Calif. (1537 Fourth St, San Rafael, CA 94901 USA) :
|b Morgan & Claypool Publishers,
|c c2008.
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|a 1 electronic text (xvi, 93 p. : col. ill.) :
|b digital file.
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|a Synthesis lectures on biomedical engineering,
|v #13
|x 1930-0336 ;
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|a Part of: Synthesis digital library of engineering and computer science.
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|a Title from PDF t.p. (viewed on Nov. 4, 2008).
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|a Series from website.
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|a Includes bibliographic references.
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|a Estimation of the effective connectivity from stationary data by structural equation modeling -- Structural equation modeling -- Simulation study -- Signal generation -- Parameter estimation -- Evaluation of performances -- Statistical analysis -- Results of the simulations -- Correct formulation of the connectivity model -- Hypothesis of a model with an arc in excess or a missing arc -- Arc in excess -- Missing arc -- Ambiguousness on an arc direction -- Discussion -- Estimation of the functional connectivity from stationary data by multivariate autoregressive methods -- Multivariate autoregressive process -- Directed transfer function -- Partial directed coherence -- Direct DTF -- Simulation study -- Signal generation -- Evaluation of performance -- Statistical analysis -- Estimation of cortical activity by the use of realistic head modeling -- The problems of conventional electroencephalogy recordings -- The high-resolution electroencephalography -- The search for the cortical sources -- Head and cortical models -- Regions of interest (ROI) -- Distributed sources estimate -- Cortical estimated waveforms -- Application : estimation of connectivity from movement-related potentials -- Subjects and experimental design -- Eeg recordings -- Selection of regions of interest (ROIs) -- Definition of the a priori model for the effective connectivity -- Statistical Evaluation of Connectivity Measurements by SEM and DTF -- Connectivity pattern representation -- Application to high-resolution EEG recordings in a cognitive task (Stroop test) -- Subjects and experimental design -- Eeg Recordings -- Application to data related to the intention of limb movements in normal subjects and in a spinal cord injured patient -- Subjects and experimental design -- Estimated connectivity patterns -- Cortical connectivity patterns in spinal cord injury -- The instantaneous estimation of the time-varying cortical -- Connectivity by adaptive multivariate estimators -- Time-varying estimation of the cortical connectivity -- The simulation study -- Statistical analysis -- Time-varying connectivity from event-related potentials -- Experimental design and Eeg recordings -- Head and cortical models -- Regions of interest.
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|a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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|a Compendex
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|a INSPEC
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|a Google scholar
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|a Google book search
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|a In the last ten years many different brain imaging devices have conveyed a lot of information about the brain functioning in different experimental conditions. In every case, the biomedical engineers, together with mathematicians, physicists and physicians are called to elaborate the signals related to the brain activity in order to extract meaningful and robust information to correlate with the external behaviour of the subjects. In such attempt, different signal processing tools used in telecommunications and other field of engineering or even social sciences have been adapted and re-used in the neuroscience field. The present book would like to offer a short presentation of several methods for the estimation of the cortical connectivity of the human brain. The methods here presented are relatively simply to implement, robust and can return valuable information about the causality of the activation of the different cortical areas in humans using non invasive electroencephalographic recordings. The knowledge of such signal processing tools will enrich the arsenal of the computational methods that a engineer or a mathematician could apply in the processing of brain signals.
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|a Also available in print.
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|a Mode of access: World Wide Web.
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|a System requirements: Adobe Acrobat Reader.
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|a Neurophysiology
|x Statistical methods.
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|a Cerebral cortex
|x Physiology
|x Statistical methods.
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|a Electroencephalography.
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|a Multivariate autoregressive model (MVAR)
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|a High resolution EEG.
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|a Directed transfer function (DTF)
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|a Partial directed coherence (PDC)
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|a Realistic head modeling.
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|a Cortical imaging.
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|a Babiloni, Fabio.
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|a Synthesis digital library of engineering and computer science.
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|a Synthesis lectures on biomedical engineering,
|v #13.
|x 1930-0336 ;
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|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.2200/S00094ED1V01Y200708BME013
|z View fulltext via EzAccess
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