BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.
Identifieur interne : 000D77 ( Main/Exploration ); précédent : 000D76; suivant : 000D78BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.
Auteurs : Ming Zhao [États-Unis] ; Prapaporn Rattanatamrong ; Jack Digiovanna ; Babak Mahmoudi ; Renato J. Figueiredo ; Justin C. Sanchez ; José C. Príncipe ; José A B. FortesSource :
- 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 ] ; 2008.
English descriptors
- KwdEn :
- Artificial Intelligence, Brain (physiology), Computers, Cybernetics (instrumentation), Cybernetics (methods), Electroencephalography (instrumentation), Electroencephalography (methods), Equipment Design, Equipment Failure Analysis, Evoked Potentials (physiology), Humans, Information Storage and Retrieval (methods), Man-Machine Systems, Software, User-Computer Interface.
- MESH :
- instrumentation : Cybernetics, Electroencephalography.
- methods : Cybernetics, Electroencephalography, Information Storage and Retrieval.
- physiology : Brain, Evoked Potentials.
- Artificial Intelligence, Computers, Equipment Design, Equipment Failure Analysis, Humans, Man-Machine Systems, Software, User-Computer Interface.
Abstract
Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.
DOI: 10.1109/IEMBS.2008.4649235
PubMed: 19162738
Affiliations:
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Le document en format XML
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Figueiredo</name></author><author><name sortKey="Sanchez, Justin C" sort="Sanchez, Justin C" uniqKey="Sanchez J" first="Justin C" last="Sanchez">Justin C. Sanchez</name></author><author><name sortKey="Principe, Jose C" sort="Principe, Jose C" uniqKey="Principe J" first="José C" last="Príncipe">José C. Príncipe</name></author><author><name sortKey="Fortes, Jose A B" sort="Fortes, Jose A B" uniqKey="Fortes J" first="José A B" last="Fortes">José A B. Fortes</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2008">2008</date><idno type="doi">10.1109/IEMBS.2008.4649235</idno><idno type="RBID">pubmed:19162738</idno><idno type="pmid">19162738</idno><idno type="wicri:Area/PubMed/Corpus">000078</idno><idno type="wicri:Area/PubMed/Curation">000078</idno><idno type="wicri:Area/PubMed/Checkpoint">000078</idno><idno type="wicri:Area/Ncbi/Merge">000073</idno><idno type="wicri:Area/Ncbi/Curation">000073</idno><idno type="wicri:Area/Ncbi/Checkpoint">000073</idno><idno type="wicri:doubleKey">1557-170X:2008:Zhao M:bmi:cyberworkstation:enabling</idno><idno type="wicri:Area/Main/Merge">000D84</idno><idno type="wicri:Area/Main/Curation">000D77</idno><idno type="wicri:Area/Main/Exploration">000D77</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.</title><author><name sortKey="Zhao, Ming" sort="Zhao, Ming" uniqKey="Zhao M" first="Ming" last="Zhao">Ming Zhao</name><affiliation wicri:level="2"><nlm:affiliation>Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA. mingzhao@ufl.edu</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL</wicri:regionArea><placeName><region type="state">Floride</region></placeName></affiliation></author><author><name sortKey="Rattanatamrong, Prapaporn" sort="Rattanatamrong, Prapaporn" uniqKey="Rattanatamrong P" first="Prapaporn" last="Rattanatamrong">Prapaporn Rattanatamrong</name></author><author><name sortKey="Digiovanna, Jack" sort="Digiovanna, Jack" uniqKey="Digiovanna J" first="Jack" last="Digiovanna">Jack Digiovanna</name></author><author><name sortKey="Mahmoudi, Babak" sort="Mahmoudi, Babak" uniqKey="Mahmoudi B" first="Babak" last="Mahmoudi">Babak Mahmoudi</name></author><author><name sortKey="Figueiredo, Renato J" sort="Figueiredo, Renato J" uniqKey="Figueiredo R" first="Renato J" last="Figueiredo">Renato J. Figueiredo</name></author><author><name sortKey="Sanchez, Justin C" sort="Sanchez, Justin C" uniqKey="Sanchez J" first="Justin C" last="Sanchez">Justin C. Sanchez</name></author><author><name sortKey="Principe, Jose C" sort="Principe, Jose C" uniqKey="Principe J" first="José C" last="Príncipe">José C. Príncipe</name></author><author><name sortKey="Fortes, Jose A B" sort="Fortes, Jose A B" uniqKey="Fortes J" first="José A B" last="Fortes">José A B. Fortes</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="2008" type="published">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial Intelligence</term><term>Brain (physiology)</term><term>Computers</term><term>Cybernetics (instrumentation)</term><term>Cybernetics (methods)</term><term>Electroencephalography (instrumentation)</term><term>Electroencephalography (methods)</term><term>Equipment Design</term><term>Equipment Failure Analysis</term><term>Evoked Potentials (physiology)</term><term>Humans</term><term>Information Storage and Retrieval (methods)</term><term>Man-Machine Systems</term><term>Software</term><term>User-Computer Interface</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Cybernetics</term><term>Electroencephalography</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Cybernetics</term><term>Electroencephalography</term><term>Information Storage and Retrieval</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term><term>Evoked Potentials</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Artificial Intelligence</term><term>Computers</term><term>Equipment Design</term><term>Equipment Failure Analysis</term><term>Humans</term><term>Man-Machine Systems</term><term>Software</term><term>User-Computer Interface</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Floride</li></region></list><tree><noCountry><name sortKey="Digiovanna, Jack" sort="Digiovanna, Jack" uniqKey="Digiovanna J" first="Jack" last="Digiovanna">Jack Digiovanna</name><name sortKey="Figueiredo, Renato J" sort="Figueiredo, Renato J" uniqKey="Figueiredo R" first="Renato J" last="Figueiredo">Renato J. Figueiredo</name><name sortKey="Fortes, Jose A B" sort="Fortes, Jose A B" uniqKey="Fortes J" first="José A B" last="Fortes">José A B. Fortes</name><name sortKey="Mahmoudi, Babak" sort="Mahmoudi, Babak" uniqKey="Mahmoudi B" first="Babak" last="Mahmoudi">Babak Mahmoudi</name><name sortKey="Principe, Jose C" sort="Principe, Jose C" uniqKey="Principe J" first="José C" last="Príncipe">José C. Príncipe</name><name sortKey="Rattanatamrong, Prapaporn" sort="Rattanatamrong, Prapaporn" uniqKey="Rattanatamrong P" first="Prapaporn" last="Rattanatamrong">Prapaporn Rattanatamrong</name><name sortKey="Sanchez, Justin C" sort="Sanchez, Justin C" uniqKey="Sanchez J" first="Justin C" last="Sanchez">Justin C. Sanchez</name></noCountry><country name="États-Unis"><region name="Floride"><name sortKey="Zhao, Ming" sort="Zhao, Ming" uniqKey="Zhao M" first="Ming" last="Zhao">Ming Zhao</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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