Evoked Potential Enhancement Using a Neurophysiologically-based Model
Objective: Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs. Method: The basic...
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Veröffentlicht in: | Methods of information in medicine 2001-01, Vol.40 (4), p.338-345 |
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description | Objective: Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs. Method: The basic procedure is a variant of the adaptive filtering approach. At the core of our method is a mathematical, but neurophysiologically-realistic, nonlinear model of the cortical structures involved in generating EEG and EP activity. The model parameters are adjusted by a genetic algorithm in such a way that the model output resembles the actually observed pre-stimulus EEG activity. When post-stimulus EEG is passed through the inverse model, enhancement of the single trial EP should, theoretically, occur. Results: Evidence was found that, in case of visual evoked potentials obtained by flashing light through closed eyelids, alpha activity continues to around 150 ms post-stimulus, at which point a low frequency potential arises, cresting 100 ms later and disappearing after another 100 ms or so. Also, it was found that an individual’s response varies considerably from trial to trial. Conclusion: The inverse modeling approach presented here is effective at enhancing single trial EP activity. One potential application is to distinguish trials that contain a response from those that do not, which could result in improved ensemble averages. |
doi_str_mv | 10.1055/s-0038-1634430 |
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H. ; Balaji Kavaipatti, A. ; Markusson, O.</creator><creatorcontrib>Jansen, B. H. ; Balaji Kavaipatti, A. ; Markusson, O.</creatorcontrib><description>Objective: Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs. Method: The basic procedure is a variant of the adaptive filtering approach. At the core of our method is a mathematical, but neurophysiologically-realistic, nonlinear model of the cortical structures involved in generating EEG and EP activity. The model parameters are adjusted by a genetic algorithm in such a way that the model output resembles the actually observed pre-stimulus EEG activity. When post-stimulus EEG is passed through the inverse model, enhancement of the single trial EP should, theoretically, occur. Results: Evidence was found that, in case of visual evoked potentials obtained by flashing light through closed eyelids, alpha activity continues to around 150 ms post-stimulus, at which point a low frequency potential arises, cresting 100 ms later and disappearing after another 100 ms or so. Also, it was found that an individual’s response varies considerably from trial to trial. Conclusion: The inverse modeling approach presented here is effective at enhancing single trial EP activity. One potential application is to distinguish trials that contain a response from those that do not, which could result in improved ensemble averages.</description><identifier>ISSN: 0026-1270</identifier><identifier>EISSN: 2511-705X</identifier><identifier>DOI: 10.1055/s-0038-1634430</identifier><identifier>PMID: 11552347</identifier><identifier>CODEN: MIMCAI</identifier><language>eng</language><publisher>Stuttgart: Schattauer Verlag für Medizin und Naturwissenschaften</publisher><subject>Adult ; Algorithms ; Biological and medical sciences ; Computer Assisted; Models ; Computerized, statistical medical data processing and models in biomedicine ; Electrodiagnosis. Electric activity recording ; Electroencephalography - methods ; Evoked Potentials ; Evoked Potentials, Visual ; Humans ; Investigative techniques, diagnostic techniques (general aspects) ; Male ; Medical management aid. 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H.</creatorcontrib><creatorcontrib>Balaji Kavaipatti, A.</creatorcontrib><creatorcontrib>Markusson, O.</creatorcontrib><title>Evoked Potential Enhancement Using a Neurophysiologically-based Model</title><title>Methods of information in medicine</title><addtitle>Methods Inf Med</addtitle><description>Objective: Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs. Method: The basic procedure is a variant of the adaptive filtering approach. At the core of our method is a mathematical, but neurophysiologically-realistic, nonlinear model of the cortical structures involved in generating EEG and EP activity. The model parameters are adjusted by a genetic algorithm in such a way that the model output resembles the actually observed pre-stimulus EEG activity. When post-stimulus EEG is passed through the inverse model, enhancement of the single trial EP should, theoretically, occur. Results: Evidence was found that, in case of visual evoked potentials obtained by flashing light through closed eyelids, alpha activity continues to around 150 ms post-stimulus, at which point a low frequency potential arises, cresting 100 ms later and disappearing after another 100 ms or so. Also, it was found that an individual’s response varies considerably from trial to trial. Conclusion: The inverse modeling approach presented here is effective at enhancing single trial EP activity. One potential application is to distinguish trials that contain a response from those that do not, which could result in improved ensemble averages.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Biological and medical sciences</subject><subject>Computer Assisted; Models</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Electrodiagnosis. Electric activity recording</subject><subject>Electroencephalography - methods</subject><subject>Evoked Potentials</subject><subject>Evoked Potentials, Visual</subject><subject>Humans</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Male</subject><subject>Medical management aid. Diagnosis aid</subject><subject>Medical sciences</subject><subject>Models, Neurological</subject><subject>Nerve Net</subject><subject>Nervous system</subject><subject>Neural Networks; Algorithms</subject><subject>Nonlinear Dynamics</subject><subject>Nonlinear; Models</subject><subject>Original Article</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Visual; Signal Processing</subject><issn>0026-1270</issn><issn>2511-705X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNrFkUtv1DAUhSMEokNhyxLNArFLsR0_kiWqpoBUHgsqsbMc52biksTB1-lo5rew4Kfi0UTQTddIlizL3z0-PifLXlJyQYkQbzEnpChzKgvOC_IoWzFBaa6I-P44WxHCZE6ZImfZM8RbQkhZEv40O6NUCFZwtco2mzv_A5r1Vx9hjM70683YmdHCkI7rG3Tjdm3Wn2EOfur26Hzvt86avt_ntcE0-Mk30D_PnrSmR3ix7OfZzdXm2-WH_PrL-4-X765zKyWJOasVKxtWCa44tA1vFdS8qjhlBaOyVJKJtqFENbQ2peXcAhGqNYJJ3gAHXpxnb066U_A_Z8CoB4cW-t6M4GfUitKSSl4l8OIE2uARA7R6Cm4wYa8p0cfgNOpjcHoJLg28WpTneoDmH74klYDXC2Aw_b8NKSSH97hCVhVLWH7CYudShvrWz2FMkTz87u8Tj7YzMZoZwl_NLsZJ73Y7fe-ugeMazNYc3Ah6hhoCOttFfQAXExhcm5rURh_0ALHzDWrrx2O5qE2wnbuDKcDg5kE7xBk0TmBT70lznNEGN0VNhdTY-V1yMPTJ4q__bFER-bC9PwzlA0I</recordid><startdate>20010101</startdate><enddate>20010101</enddate><creator>Jansen, B. H.</creator><creator>Balaji Kavaipatti, A.</creator><creator>Markusson, O.</creator><general>Schattauer Verlag für Medizin und Naturwissenschaften</general><general>Schattauer GmbH</general><general>Schattauer</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20010101</creationdate><title>Evoked Potential Enhancement Using a Neurophysiologically-based Model</title><author>Jansen, B. H. ; Balaji Kavaipatti, A. ; Markusson, O.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c660t-2b728d295474efd4f7eb499412321687625fd107d1ba8c44ce057fa5264de4e43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2001</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Biological and medical sciences</topic><topic>Computer Assisted; Models</topic><topic>Computerized, statistical medical data processing and models in biomedicine</topic><topic>Electrodiagnosis. Electric activity recording</topic><topic>Electroencephalography - methods</topic><topic>Evoked Potentials</topic><topic>Evoked Potentials, Visual</topic><topic>Humans</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Male</topic><topic>Medical management aid. Diagnosis aid</topic><topic>Medical sciences</topic><topic>Models, Neurological</topic><topic>Nerve Net</topic><topic>Nervous system</topic><topic>Neural Networks; Algorithms</topic><topic>Nonlinear Dynamics</topic><topic>Nonlinear; Models</topic><topic>Original Article</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Visual; Signal Processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jansen, B. H.</creatorcontrib><creatorcontrib>Balaji Kavaipatti, A.</creatorcontrib><creatorcontrib>Markusson, O.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Methods of information in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jansen, B. H.</au><au>Balaji Kavaipatti, A.</au><au>Markusson, O.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evoked Potential Enhancement Using a Neurophysiologically-based Model</atitle><jtitle>Methods of information in medicine</jtitle><addtitle>Methods Inf Med</addtitle><date>2001-01-01</date><risdate>2001</risdate><volume>40</volume><issue>4</issue><spage>338</spage><epage>345</epage><pages>338-345</pages><issn>0026-1270</issn><eissn>2511-705X</eissn><coden>MIMCAI</coden><abstract>Objective: Single trial evoked potentials (EP) are generally obscured by the much larger spontaneous or background electroencephalogram (EEG). A novel method was developed to enhance single trial EPs. The potential of this approach was explored using actual flash evoked visual EPs. Method: The basic procedure is a variant of the adaptive filtering approach. At the core of our method is a mathematical, but neurophysiologically-realistic, nonlinear model of the cortical structures involved in generating EEG and EP activity. The model parameters are adjusted by a genetic algorithm in such a way that the model output resembles the actually observed pre-stimulus EEG activity. When post-stimulus EEG is passed through the inverse model, enhancement of the single trial EP should, theoretically, occur. Results: Evidence was found that, in case of visual evoked potentials obtained by flashing light through closed eyelids, alpha activity continues to around 150 ms post-stimulus, at which point a low frequency potential arises, cresting 100 ms later and disappearing after another 100 ms or so. Also, it was found that an individual’s response varies considerably from trial to trial. Conclusion: The inverse modeling approach presented here is effective at enhancing single trial EP activity. 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subjects | Adult Algorithms Biological and medical sciences Computer Assisted Models Computerized, statistical medical data processing and models in biomedicine Electrodiagnosis. Electric activity recording Electroencephalography - methods Evoked Potentials Evoked Potentials, Visual Humans Investigative techniques, diagnostic techniques (general aspects) Male Medical management aid. Diagnosis aid Medical sciences Models, Neurological Nerve Net Nervous system Neural Networks Algorithms Nonlinear Dynamics Nonlinear Models Original Article Signal Processing, Computer-Assisted Visual Signal Processing |
title | Evoked Potential Enhancement Using a Neurophysiologically-based Model |
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