Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies.

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies. Research Abstract Details 

Research Abstract Table of Contents

Jump to the:

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies. Abstract Text:

L Astolfi,F Cincotti,D Mattia,M Mattiocco,F De Vico Fallani,A Colosimo,M G Marciani,W Hesse,L Zemanova,G Z Lopez,J Kurths,C Zhou,F Babiloni,L Astolfi,F Cincotti,D Mattia,M Mattiocco,F De Vico Fallani,A Colosimo,M G Marciani,W Hesse,L Zemanova,G Zamora Lopez,J Kurths,C Zhou,F Babiloni,

The Directed Transfer Function (DTF) and the Partial Directed Coherence (PDC) are frequency-domain estimators, based on the multivariate autoregressive modelling (MVAR) of time series, that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods requires the stationary of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR). This approach will allow the observation of transient influences between the cortical areas during the execution of a task. Time-varying DTF and PDC were obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square (RLS) algorithm, taking into consideration a set of EEG epochs. Simulations were performed under different levels of Signal to Noise Ratio (SNR), number of trials (TRIALS) and frequency bands (BAND), and of different values of the RLS adaptation factor adopted (factor C). The results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of SNR ad number of trials. Moreover, the capability of follow the rapid changes in connectivity is highly increased by the number of trials at disposal, and by the right choice of the value adopted for the adaptation factor C. The results of the simulation study indicate that DTF and PDC computed on adaptive MVAR can be effectively used to estimate time-varying patterns of functional connectivity between cortical activations, under general conditions met in practical EEG recordings.

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies. Publishing Authors By Initials

L Astolfi,F Cincotti,D Mattia,M Mattiocco,F De Vico Fallani,A Colosimo,MG Marciani,W Hesse,L Zemanova,GZ Lopez,J Kurths,C Zhou,F Babiloni,L Astolfi,F Cincotti,D Mattia,M Mattiocco,F De Vico Fallani,A Colosimo,MG Marciani,W Hesse,L Zemanova,GZ Lopez,J Kurths,C Zhou,F Babiloni,

For similar abstracts research abstracts see: abstracts research

PUBMED ID PMID:

MEDLINE DATE:

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Conference proceedings : ... Annual International

VOLUME: 1

Page Numbers: 2446-9

Journal Abbreviation:

ISSN: 1557-170X

DAY: 23

MONTH: 10

YEAR: 2006

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies. Information

Number of References:

LANGUAGE: eng

NlmUniqueID: 101243413

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies. Keywords Mesh Terms:

KEYWORDS:

MESH TERMS:

Chemical & Substance for Abstract: Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies. Information

Substance Name:

Registry Number:

Grant and Affiliation Information for Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies.

AFFILIATION: IRCCS, Fondazione Santa Lucia, Rome.

Country: United States

AGENCY:

GRANT:

ACRONYM:

MEDLINETA: Conf Proc IEEE Eng Med Biol So

REFSOURCE:

DATABASENAME:

ACCESSION NUMBER:

Number Hits: 0

Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies Related Publications

• A local neural classifier for the recognition of EEG patterns associated to mental tasks.
• Assessing cortical functional connectivity by linear inverse estimation and directed transfer function: simulations and application to real data.
• Brain connectivity structure in spinal cord injured: evaluation by graph analysis.
• Comparison of different cortical connectivity estimators for high-resolution EEG recordings.
• Comparison of different multivariate methods for the estimation of cortical connectivity: simulations and applications to EEG data.
• Cortical Network Dynamics during Foot Movements.
• Cortical connectivity patterns during imagination of limb movements in normal subjects and in a spinal cord injured patient.
• Cortical functional connectivity networks in normal and spinal cord injured patients: Evaluation by graph analysis.
• Dynamic functional coupling of high resolution EEG potentials related to unilateral internally triggered one-digit movements.
• Estimate of causality between independent cortical spatial patterns during movement volition in spinal cord injured patients.
• Estimation of the time-varying cortical connectivity patterns by the adaptive multivariate estimators in high resolution EEG studies.
• Extracting information from cortical connectivity patterns estimated from high resolution EEG recordings: a theoretical graph approach.
• Features extraction from time-varying cortical networks adopting a theoretical graph approach.
• High-resolution EEG techniques for brain-computer interface applications.
• Imaging functional brain connectivity patterns from high-resolution EEG and fMRI via graph theory.
• Non-invasive brain-computer interface system: Towards its application as assistive technology.
• Spatial enhancement of EEG data by surface Laplacian estimation: the use of magnetic resonance imaging-based head models.
• Structure of the cortical networks during successful memory encoding in TV commercials.
• Time-varying cortical connectivity by adaptive multivariate estimators applied to a combined foot-lips movement.
• Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators.