A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis
Identifieur interne : 001554 ( Main/Exploration ); précédent : 001553; suivant : 001555A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis
Auteurs : Joyce Chiang [Canada] ; Z. Jane Wang [Canada] ; Martin J. Mckeown [Canada]Source :
- IEEE transactions on signal processing [ 1053-587X ] ; 2012.
Descripteurs français
- Pascal (Inist)
- Modèle autorégressif, Electroencéphalographie, Connexité, Volume, Méthode section divisée, Espace état, Causalité, Processus multivarié, Processus autorégressif, Bruit non gaussien, Maximum vraisemblance, Précision, Tâche poursuite, Rétroaction, Cohérence, Extraction information, Maladie, Traitement signal.
- Wicri :
- topic : Maladie.
English descriptors
- KwdEn :
- Accuracy, Autoregressive model, Autoregressive processes, Causality, Coherence, Connectedness, Disease, Electroencephalography, Feedback regulation, Information extraction, Maximum likelihood, Multistage method, Multivariate process, Non gaussian noise, Signal processing, State space, Tracking task, Volume.
Abstract
Studying brain connectivity has provided new insights to the understanding of brain function. While connectivity measures are conventionally computed from electroencephalogram (EEG) signals directly, the presence of volume conduction represents a serious confound affecting interpretation of results. A common solution is to use a two-stage approach which involves estimating underlying brain sources from scalp EEG recordings and subsequently estimating the connectivity between the inferred sources. Recently, a state-space framework which jointly models the instantaneous mixing effects of volume conduction and the causal relationships between underlying brain sources is proposed. In this paper, we extend the state-space framework and model the source activity by a generalized multivariate autoregressive (mAR) process with possibly non-Gaussian noise. A maximum likelihood estimation approach is developed which allows simultaneous estimation of both the mixing matrix and AR model parameters directly from scalp EEG. The proposed technique was verified with simulated EEG data generated using the single-shell spherical head model and demonstrated improved estimation accuracies compared to conventional two-stage connectivity estimation approaches. Furthermore, the proposed technique was applied to EEG data collected from normal and Parkinson's subjects performing a right-handed force-tracking task with differing amounts of visual feedback. The partial directed coherence (PDC) between sources showed significant differences between groups and conditions. These results suggest that the proposed technique is a powerful method to extract connectivity information from EEG recordings.
Affiliations:
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Jane Wang</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Electrical and Computer Engineering, The University of British Columbia</s1><s2>Vancouver, BC V6T 1Z4</s2><s3>CAN</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Canada</country><wicri:noRegion>Vancouver, BC V6T 1Z4</wicri:noRegion></affiliation></author><author><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J." last="Mckeown">Martin J. Mckeown</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Medicine (Neurology), The University of British Columbia</s1><s2>Vancouver, BC V6T 2B5</s2><s3>CAN</s3><sZ>3 aut.</sZ></inist:fA14><country>Canada</country><wicri:noRegion>Vancouver, BC V6T 2B5</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">12-0104801</idno><date when="2012">2012</date><idno type="stanalyst">PASCAL 12-0104801 INIST</idno><idno type="RBID">Pascal:12-0104801</idno><idno type="wicri:Area/PascalFrancis/Corpus">000226</idno><idno type="wicri:Area/PascalFrancis/Curation">000A26</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000201</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">000201</idno><idno type="wicri:doubleKey">1053-587X:2012:Chiang J:a:generalized:multivariate</idno><idno type="wicri:Area/Main/Merge">001615</idno><idno type="wicri:Area/Main/Curation">001554</idno><idno type="wicri:Area/Main/Exploration">001554</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis</title><author><name sortKey="Chiang, Joyce" sort="Chiang, Joyce" uniqKey="Chiang J" first="Joyce" last="Chiang">Joyce Chiang</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Electrical and Computer Engineering, The University of British Columbia</s1><s2>Vancouver, BC V6T 1Z4</s2><s3>CAN</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Canada</country><wicri:noRegion>Vancouver, BC V6T 1Z4</wicri:noRegion></affiliation></author><author><name sortKey="Wang, Z Jane" sort="Wang, Z Jane" uniqKey="Wang Z" first="Z. Jane" last="Wang">Z. Jane Wang</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Electrical and Computer Engineering, The University of British Columbia</s1><s2>Vancouver, BC V6T 1Z4</s2><s3>CAN</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ></inist:fA14><country>Canada</country><wicri:noRegion>Vancouver, BC V6T 1Z4</wicri:noRegion></affiliation></author><author><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J." last="Mckeown">Martin J. Mckeown</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Department of Medicine (Neurology), The University of British Columbia</s1><s2>Vancouver, BC V6T 2B5</s2><s3>CAN</s3><sZ>3 aut.</sZ></inist:fA14><country>Canada</country><wicri:noRegion>Vancouver, BC V6T 2B5</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">IEEE transactions on signal processing</title><title level="j" type="abbreviated">IEEE trans. signal process.</title><idno type="ISSN">1053-587X</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE transactions on signal processing</title><title level="j" type="abbreviated">IEEE trans. signal process.</title><idno type="ISSN">1053-587X</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Accuracy</term><term>Autoregressive model</term><term>Autoregressive processes</term><term>Causality</term><term>Coherence</term><term>Connectedness</term><term>Disease</term><term>Electroencephalography</term><term>Feedback regulation</term><term>Information extraction</term><term>Maximum likelihood</term><term>Multistage method</term><term>Multivariate process</term><term>Non gaussian noise</term><term>Signal processing</term><term>State space</term><term>Tracking task</term><term>Volume</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Modèle autorégressif</term><term>Electroencéphalographie</term><term>Connexité</term><term>Volume</term><term>Méthode section divisée</term><term>Espace état</term><term>Causalité</term><term>Processus multivarié</term><term>Processus autorégressif</term><term>Bruit non gaussien</term><term>Maximum vraisemblance</term><term>Précision</term><term>Tâche poursuite</term><term>Rétroaction</term><term>Cohérence</term><term>Extraction information</term><term>Maladie</term><term>Traitement signal</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Maladie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Studying brain connectivity has provided new insights to the understanding of brain function. While connectivity measures are conventionally computed from electroencephalogram (EEG) signals directly, the presence of volume conduction represents a serious confound affecting interpretation of results. A common solution is to use a two-stage approach which involves estimating underlying brain sources from scalp EEG recordings and subsequently estimating the connectivity between the inferred sources. Recently, a state-space framework which jointly models the instantaneous mixing effects of volume conduction and the causal relationships between underlying brain sources is proposed. In this paper, we extend the state-space framework and model the source activity by a generalized multivariate autoregressive (mAR) process with possibly non-Gaussian noise. A maximum likelihood estimation approach is developed which allows simultaneous estimation of both the mixing matrix and AR model parameters directly from scalp EEG. The proposed technique was verified with simulated EEG data generated using the single-shell spherical head model and demonstrated improved estimation accuracies compared to conventional two-stage connectivity estimation approaches. Furthermore, the proposed technique was applied to EEG data collected from normal and Parkinson's subjects performing a right-handed force-tracking task with differing amounts of visual feedback. The partial directed coherence (PDC) between sources showed significant differences between groups and conditions. These results suggest that the proposed technique is a powerful method to extract connectivity information from EEG recordings.</div></front></TEI><affiliations><list><country><li>Canada</li></country></list><tree><country name="Canada"><noRegion><name sortKey="Chiang, Joyce" sort="Chiang, Joyce" uniqKey="Chiang J" first="Joyce" last="Chiang">Joyce Chiang</name></noRegion><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J." last="Mckeown">Martin J. Mckeown</name><name sortKey="Wang, Z Jane" sort="Wang, Z Jane" uniqKey="Wang Z" first="Z. Jane" last="Wang">Z. Jane Wang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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