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A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis

Identifieur interne : 000226 ( PascalFrancis/Corpus ); précédent : 000225; suivant : 000227

A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis

Auteurs : Joyce Chiang ; Z. Jane Wang ; Martin J. Mckeown

Source :

IEEE transactions on signal processing [ 1053-587X ] ; 2012.

RBID : Pascal:12-0104801

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.

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.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 12-0104801 INIST
ET :	A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis
AU :	CHIANG (Joyce); WANG (Z. Jane); MCKEOWN (Martin J.)
AF :	Department of Electrical and Computer Engineering, The University of British Columbia/Vancouver, BC V6T 1Z4/Canada (1 aut., 2 aut.); Department of Medicine (Neurology), The University of British Columbia/Vancouver, BC V6T 2B5/Canada (3 aut.)
DT :	Publication en série; Niveau analytique
SO :	IEEE transactions on signal processing; ISSN 1053-587X; Coden ITPRED; Etats-Unis; Da. 2012; Vol. 60; No. 1; Pp. 453-465; Bibl. 36 ref.
LA :	Anglais
EA :	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.
CC :	002B28C; 001D04A04A2; 001D04A03
FD :	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
FG :	Traitement information
ED :	Autoregressive model; Electroencephalography; Connectedness; Volume; Multistage method; State space; Causality; Multivariate process; Autoregressive processes; Non gaussian noise; Maximum likelihood; Accuracy; Tracking task; Feedback regulation; Coherence; Information extraction; Disease; Signal processing
EG :	Information processing
SD :	Modelo autorregresivo; Electroencefalografía; Conexidad; Volumen; Espacio estado; Causalidad; Proceso multivariable; Ruido no gaussiano; Maxima verosimilitud; Precisión; Tarea persecución; Retroacción; Coherencia; Extracción información; Enfermedad; Procesamiento señal
LO :	INIST-222E3.354000508869520350
ID :	12-0104801

Links to Exploration step

Pascal:12-0104801

Le document en format XML

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<fC03 i1="07" i2="X" l="FRE"><s0>Causalité</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Causality</s0>
<s5>07</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Causalidad</s0>
<s5>07</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Processus multivarié</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Multivariate process</s0>
<s5>08</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Proceso multivariable</s0>
<s5>08</s5>
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<fC03 i1="09" i2="3" l="FRE"><s0>Processus autorégressif</s0>
<s5>09</s5>
</fC03>
<fC03 i1="09" i2="3" l="ENG"><s0>Autoregressive processes</s0>
<s5>09</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Bruit non gaussien</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Non gaussian noise</s0>
<s5>10</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Ruido no gaussiano</s0>
<s5>10</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Maximum vraisemblance</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Maximum likelihood</s0>
<s5>11</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Maxima verosimilitud</s0>
<s5>11</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Précision</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Accuracy</s0>
<s5>12</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Precisión</s0>
<s5>12</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Tâche poursuite</s0>
<s5>13</s5>
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<s5>13</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Tarea persecución</s0>
<s5>13</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Rétroaction</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Feedback regulation</s0>
<s5>14</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Retroacción</s0>
<s5>14</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Cohérence</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Coherence</s0>
<s5>15</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Coherencia</s0>
<s5>15</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Extraction information</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Information extraction</s0>
<s5>16</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Extracción información</s0>
<s5>16</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Maladie</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Disease</s0>
<s5>17</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Enfermedad</s0>
<s5>17</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Traitement signal</s0>
<s5>46</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Signal processing</s0>
<s5>46</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Procesamiento señal</s0>
<s5>46</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Traitement information</s0>
<s5>18</s5>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Information processing</s0>
<s5>18</s5>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Procesamiento información</s0>
<s5>18</s5>
</fC07>
<fN21><s1>079</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 12-0104801 INIST</NO>
<ET>A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis</ET>
<AU>CHIANG (Joyce); WANG (Z. Jane); MCKEOWN (Martin J.)</AU>
<AF>Department of Electrical and Computer Engineering, The University of British Columbia/Vancouver, BC V6T 1Z4/Canada (1 aut., 2 aut.); Department of Medicine (Neurology), The University of British Columbia/Vancouver, BC V6T 2B5/Canada (3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>IEEE transactions on signal processing; ISSN 1053-587X; Coden ITPRED; Etats-Unis; Da. 2012; Vol. 60; No. 1; Pp. 453-465; Bibl. 36 ref.</SO>
<LA>Anglais</LA>
<EA>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.</EA>
<CC>002B28C; 001D04A04A2; 001D04A03</CC>
<FD>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</FD>
<FG>Traitement information</FG>
<ED>Autoregressive model; Electroencephalography; Connectedness; Volume; Multistage method; State space; Causality; Multivariate process; Autoregressive processes; Non gaussian noise; Maximum likelihood; Accuracy; Tracking task; Feedback regulation; Coherence; Information extraction; Disease; Signal processing</ED>
<EG>Information processing</EG>
<SD>Modelo autorregresivo; Electroencefalografía; Conexidad; Volumen; Espacio estado; Causalidad; Proceso multivariable; Ruido no gaussiano; Maxima verosimilitud; Precisión; Tarea persecución; Retroacción; Coherencia; Extracción información; Enfermedad; Procesamiento señal</SD>
<LO>INIST-222E3.354000508869520350</LO>
<ID>12-0104801</ID>
</server>
</inist>
</record>

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	La maladie de Parkinson au Canada (serveur d'exploration)
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La maladie de Parkinson au Canada (serveur d'exploration)

A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis

A Generalized Multivariate Autoregressive (GmAR)-Based Approach for EEG Source Connectivity Analysis

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