EEG source extraction by autoregressive source separation reveals abnormal synchronization in Parkinson's disease.
Identifieur interne : 001E52 ( Main/Exploration ); précédent : 001E51; suivant : 001E53EEG source extraction by autoregressive source separation reveals abnormal synchronization in Parkinson's disease.
Auteurs : Joyce Chiang [Canada] ; Z. Wang ; Martin J. MckeownSource :
- Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference [ 1557-170X ] ; 2009.
English descriptors
- KwdEn :
- MESH :
- methods : Brain Mapping, Electroencephalography.
- physiology : Brain.
- physiopathology : Brain, Parkinson Disease.
- Algorithms, Humans, Likelihood Functions, Models, Neurological, Reference Values, Regression Analysis, Signal Transduction, Software.
Abstract
Recent research efforts in studying brain connectivity has provided new perspectives to understanding of neurophysiology of brain function. Connectivity measures are typically computed from electroencephalogram (EEG) signals, yet the presence of volume conduction makes interpretation of results difficult. One possible alternative is to model the connectivity in the source space. In this study, we proposed a novel source separation technique in which EEG signals are represented as a state-space framework. The framework jointly models the underlying brain sources and the connectivity between them in the form of a generalized autoregressive (AR) process. The proposed technique was applied to real EEG data collected from normal and Parkinson's patients during a motor task. The extracted sources revealed the abnormal beta activity in Parkinson's subjects and showed similar biological networks as previous studies.
DOI: 10.1109/IEMBS.2009.5332613
PubMed: 19963527
Affiliations:
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Le document en format XML
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Mckeown</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2009">2009</date><idno type="RBID">pubmed:19963527</idno><idno type="pmid">19963527</idno><idno type="doi">10.1109/IEMBS.2009.5332613</idno><idno type="wicri:Area/PubMed/Corpus">000F13</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000F13</idno><idno type="wicri:Area/PubMed/Curation">000F13</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000F13</idno><idno type="wicri:Area/PubMed/Checkpoint">000F13</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000F13</idno><idno type="wicri:Area/Ncbi/Merge">000B84</idno><idno type="wicri:Area/Ncbi/Curation">000B84</idno><idno type="wicri:Area/Ncbi/Checkpoint">000B84</idno><idno type="wicri:doubleKey">1557-170X:2009:Chiang J:eeg:source:extraction</idno><idno type="wicri:Area/Main/Merge">002004</idno><idno type="wicri:Area/Main/Curation">001E52</idno><idno type="wicri:Area/Main/Exploration">001E52</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">EEG source extraction by autoregressive source separation reveals abnormal synchronization in Parkinson's disease.</title><author><name sortKey="Chiang, Joyce" sort="Chiang, Joyce" uniqKey="Chiang J" first="Joyce" last="Chiang">Joyce Chiang</name><affiliation wicri:level="1"><nlm:affiliation>Department of Electrical and Computer Engineering, University of British Columbia, Canada. joycehc@interchange.ubc.ca</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Department of Electrical and Computer Engineering, University of British Columbia</wicri:regionArea><wicri:noRegion>University of British Columbia</wicri:noRegion></affiliation></author><author><name sortKey="Wang, Z" sort="Wang, Z" uniqKey="Wang Z" first="Z" last="Wang">Z. Wang</name></author><author><name sortKey="Mckeown, Martin J" sort="Mckeown, Martin J" uniqKey="Mckeown M" first="Martin J" last="Mckeown">Martin J. Mckeown</name></author></analytic><series><title level="j">Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference</title><idno type="ISSN">1557-170X</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Brain (physiology)</term><term>Brain (physiopathology)</term><term>Brain Mapping (methods)</term><term>Electroencephalography (methods)</term><term>Humans</term><term>Likelihood Functions</term><term>Models, Neurological</term><term>Parkinson Disease (physiopathology)</term><term>Reference Values</term><term>Regression Analysis</term><term>Signal Transduction</term><term>Software</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Brain Mapping</term><term>Electroencephalography</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Brain</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Humans</term><term>Likelihood Functions</term><term>Models, Neurological</term><term>Reference Values</term><term>Regression Analysis</term><term>Signal Transduction</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Recent research efforts in studying brain connectivity has provided new perspectives to understanding of neurophysiology of brain function. Connectivity measures are typically computed from electroencephalogram (EEG) signals, yet the presence of volume conduction makes interpretation of results difficult. One possible alternative is to model the connectivity in the source space. In this study, we proposed a novel source separation technique in which EEG signals are represented as a state-space framework. The framework jointly models the underlying brain sources and the connectivity between them in the form of a generalized autoregressive (AR) process. The proposed technique was applied to real EEG data collected from normal and Parkinson's patients during a motor task. The extracted sources revealed the abnormal beta activity in Parkinson's subjects and showed similar biological networks as previous studies.</div></front></TEI><affiliations><list><country><li>Canada</li></country></list><tree><noCountry><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" sort="Wang, Z" uniqKey="Wang Z" first="Z" last="Wang">Z. Wang</name></noCountry><country name="Canada"><noRegion><name sortKey="Chiang, Joyce" sort="Chiang, Joyce" uniqKey="Chiang J" first="Joyce" last="Chiang">Joyce Chiang</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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