A Joint Multimodal Group Analysis Framework for Modeling Corticomuscular Activity : MULTIMODAL BIOMEDICAL IMAGING: ALGORITHMS AND APPLICATIONS
Identifieur interne : 001086 ( Main/Exploration ); précédent : 001085; suivant : 001087A Joint Multimodal Group Analysis Framework for Modeling Corticomuscular Activity : MULTIMODAL BIOMEDICAL IMAGING: ALGORITHMS AND APPLICATIONS
Auteurs : XUN CHEN [Canada] ; XIANG CHEN [République populaire de Chine] ; Rabab Kreidieh Ward [Canada] ; Z. Jane Wang [Canada]Source :
- IEEE transactions on multimedia [ 1520-9210 ] ; 2013.
Descripteurs français
- Pascal (Inist)
- Interface multimodale, Analyse scène, Type donnée, Application médicale, Corrélation, Analyse donnée, Fusion donnée, Activité, Electroencéphalographie, Electromyographie, Contrôle moteur, Moindre carré partiel, Mélangeage, Modèle multiple, Variabilité, Pathologie du système nerveux, Analyse modale, Maladie de Parkinson, ., Modèle donnée.
English descriptors
- KwdEn :
- Activity, Correlation, Data analysis, Data fusion, Data models, Data type, Electroencephalography, Electromyography, Medical application, Mixing, Modal analysis, Motor control, Multimodal interface, Multimodel, Nervous system diseases, Parkinson disease, Partial least squares, Scene analysis, Variability.
Abstract
Corticomuscular coupling analysis based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. Two probably most popular methods are the pair-wise magnitude-squared coherence (MSC) between EEG and simultaneously-recorded EMG signals, and partial least square (PLS). Unfortunately, MSC and PLS generally deal with only two types of data sets at the same time, while we may need to analyze more than two types of data sets. Moreover, it is not straightforward to extend MSC to the group level for combining results across subjects. Also, PLS can have the information mixing problem since only the variations in one data set are used to predict the other data set. To address these concerns, we propose a joint multimodal analysis framework for corticomuscular coupling analysis. The proposed framework models multiple data spaces simultaneously in a multidirectional fashion. Furthermore, to address the inter-subject variability concern in real-world medical applications, we extend the proposed framework from the individual subject level to the group level to obtain common corticomuscular coupling patterns across subjects. We apply the proposed framework to concurrent EEG, EMG and behavior data collected in a Parkinson's disease (PD) study. The results reveal several highly correlated temporal patterns among the three types of signals and their corresponding spatial activation patterns. In PD subjects, there are enhanced connections between occipital region and other regions, which is consistent with the previous medical finding. The proposed framework is a promising technique for performing multi-subject and multi-modal data analysis.
Affiliations:
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Jane" last="Wang">Z. Jane Wang</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Electrical and Computer Engineering, University of British Columbia</s1><s2>Vancouver, BC V6T 1Z4</s2><s3>CAN</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ><sZ>4 aut.</sZ></inist:fA14><country>Canada</country><wicri:noRegion>Vancouver, BC V6T 1Z4</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">IEEE transactions on multimedia</title><title level="j" type="abbreviated">IEEE trans. multimedia</title><idno type="ISSN">1520-9210</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">IEEE transactions on multimedia</title><title level="j" type="abbreviated">IEEE trans. multimedia</title><idno type="ISSN">1520-9210</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Activity</term><term>Correlation</term><term>Data analysis</term><term>Data fusion</term><term>Data models</term><term>Data type</term><term>Electroencephalography</term><term>Electromyography</term><term>Medical application</term><term>Mixing</term><term>Modal analysis</term><term>Motor control</term><term>Multimodal interface</term><term>Multimodel</term><term>Nervous system diseases</term><term>Parkinson disease</term><term>Partial least squares</term><term>Scene analysis</term><term>Variability</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Interface multimodale</term><term>Analyse scène</term><term>Type donnée</term><term>Application médicale</term><term>Corrélation</term><term>Analyse donnée</term><term>Fusion donnée</term><term>Activité</term><term>Electroencéphalographie</term><term>Electromyographie</term><term>Contrôle moteur</term><term>Moindre carré partiel</term><term>Mélangeage</term><term>Modèle multiple</term><term>Variabilité</term><term>Pathologie du système nerveux</term><term>Analyse modale</term><term>Maladie de Parkinson</term><term>.</term><term>Modèle donnée</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Corticomuscular coupling analysis based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. Two probably most popular methods are the pair-wise magnitude-squared coherence (MSC) between EEG and simultaneously-recorded EMG signals, and partial least square (PLS). Unfortunately, MSC and PLS generally deal with only two types of data sets at the same time, while we may need to analyze more than two types of data sets. Moreover, it is not straightforward to extend MSC to the group level for combining results across subjects. Also, PLS can have the information mixing problem since only the variations in one data set are used to predict the other data set. To address these concerns, we propose a joint multimodal analysis framework for corticomuscular coupling analysis. The proposed framework models multiple data spaces simultaneously in a multidirectional fashion. Furthermore, to address the inter-subject variability concern in real-world medical applications, we extend the proposed framework from the individual subject level to the group level to obtain common corticomuscular coupling patterns across subjects. We apply the proposed framework to concurrent EEG, EMG and behavior data collected in a Parkinson's disease (PD) study. The results reveal several highly correlated temporal patterns among the three types of signals and their corresponding spatial activation patterns. In PD subjects, there are enhanced connections between occipital region and other regions, which is consistent with the previous medical finding. The proposed framework is a promising technique for performing multi-subject and multi-modal data analysis.</div></front></TEI><affiliations><list><country><li>Canada</li><li>République populaire de Chine</li></country></list><tree><country name="Canada"><noRegion><name sortKey="Xun Chen" sort="Xun Chen" uniqKey="Xun Chen" last="Xun Chen">XUN CHEN</name></noRegion><name sortKey="Kreidieh Ward, Rabab" sort="Kreidieh Ward, Rabab" uniqKey="Kreidieh Ward R" first="Rabab" last="Kreidieh Ward">Rabab Kreidieh Ward</name><name sortKey="Wang, Z Jane" sort="Wang, Z Jane" uniqKey="Wang Z" first="Z. Jane" last="Wang">Z. Jane Wang</name></country><country name="République populaire de Chine"><noRegion><name sortKey="Xiang Chen" sort="Xiang Chen" uniqKey="Xiang Chen" last="Xiang Chen">XIANG CHEN</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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