Loading…
Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans
We apply a recently developed multivariate statistical data analysis technique-so called blind source separation (BSS) by independent component analysis-to process magnetoencephalogram recordings of near-DC fields. The extraction of near-DC fields from MEG recordings has great relevance for medical...
Saved in:
| Published in: | IEEE transactions on biomedical engineering 2000-05, Vol.47 (5), p.594-599 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites Items that cite this one |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | cdi_FETCH-LOGICAL-c485t-617cdf7b5c4da9de4664e8e183f58dee62b23cd94a57ae34fb64a5d5206710e33 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c485t-617cdf7b5c4da9de4664e8e183f58dee62b23cd94a57ae34fb64a5d5206710e33 |
| container_end_page | 599 |
| container_issue | 5 |
| container_start_page | 594 |
| container_title | IEEE transactions on biomedical engineering |
| container_volume | 47 |
| creator | Wubbeler, G. Ziehe, A. Mackert, B.-M. Muller, K.-R. Trahms, L. Curio, C. |
| description | We apply a recently developed multivariate statistical data analysis technique-so called blind source separation (BSS) by independent component analysis-to process magnetoencephalogram recordings of near-DC fields. The extraction of near-DC fields from MEG recordings has great relevance for medical applications since slowly varying DC-phenomena have been found, e.g., in cerebral anoxia and spreading depression in animals. Comparing several BSS approaches, it turns out that an algorithm based on temporal decorrelation successfully extracted a DC-component which was induced in the auditory cortex by presentation of music. The task is challenging because of the limited amount of available data and the corruption by outliers, which makes it an interesting real-world testbed for studying the robustness of ICA methods. |
| doi_str_mv | 10.1109/10.841331 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_proquest_miscellaneous_71181437</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>841331</ieee_id><sourcerecordid>2435166971</sourcerecordid><originalsourceid>FETCH-LOGICAL-c485t-617cdf7b5c4da9de4664e8e183f58dee62b23cd94a57ae34fb64a5d5206710e33</originalsourceid><addsrcrecordid>eNqN0UFrFTEQAOAgin2tHrx6kKVIi4fVzCbZnT2W11oLBS96dclLZm3KbvJM3hbevzfLPlR6UC-ZCfmYDDOMvQL-HoC3H3JECULAE7YCpbCslICnbMU5YNlWrTxixynd56tEWT9nR8BRAXKxYt9uvKUt5cPvChPGbfBzpr0e9smlIvSFD975B53cAw37IpIJ0ZLNOO6c0UMx6u-eclpcrsve0WBT4XxxN43apxfsWa-HRC8P8YR9_Xj1Zf2pvP18fbO-uC2NRLUra2iM7ZuNMtLq1pKsa0lIgKJXaInqalMJY1upVaNJyH5T59SqitcNcBLihJ0vdbcx_Jgo7brRJUPDoD2FKXWICCBQVFme_VU2AAhSNP-EVR45_y8IEmoh5iZPH8H7MMU86bk_VQmJHDN6tyATQ0qR-m4b3ajjvgPezdue47LtbN8cCk6bkewfcllvBm8PQKe8qj5qb1z67YRsFMwzeb0wR0S_Xg-f_ASdkbiD</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>885234808</pqid></control><display><type>article</type><title>Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans</title><source>IEEE Xplore (Online service)</source><creator>Wubbeler, G. ; Ziehe, A. ; Mackert, B.-M. ; Muller, K.-R. ; Trahms, L. ; Curio, C.</creator><creatorcontrib>Wubbeler, G. ; Ziehe, A. ; Mackert, B.-M. ; Muller, K.-R. ; Trahms, L. ; Curio, C.</creatorcontrib><description>We apply a recently developed multivariate statistical data analysis technique-so called blind source separation (BSS) by independent component analysis-to process magnetoencephalogram recordings of near-DC fields. The extraction of near-DC fields from MEG recordings has great relevance for medical applications since slowly varying DC-phenomena have been found, e.g., in cerebral anoxia and spreading depression in animals. Comparing several BSS approaches, it turns out that an algorithm based on temporal decorrelation successfully extracted a DC-component which was induced in the auditory cortex by presentation of music. The task is challenging because of the limited amount of available data and the corruption by outliers, which makes it an interesting real-world testbed for studying the robustness of ICA methods.</description><identifier>ISSN: 0018-9294</identifier><identifier>EISSN: 1558-2531</identifier><identifier>DOI: 10.1109/10.841331</identifier><identifier>PMID: 10851803</identifier><identifier>CODEN: IEBEAX</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Acoustic Stimulation ; Algorithms ; Auditory Cortex - physiology ; Bioelectric potentials ; Biological and medical sciences ; Blind source separation ; Data analysis ; Data mining ; Evoked Potentials, Auditory - physiology ; Feature extraction ; Humans ; Independent component analysis ; Investigative techniques, diagnostic techniques (general aspects) ; Magnetic analysis ; Magnetic recording ; Magnetic separation ; Magnetoencephalography ; Medical sciences ; Medical services ; Nervous system ; Neurophysiology ; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques ; Signal Processing, Computer-Assisted ; Source separation ; Statistical methods</subject><ispartof>IEEE transactions on biomedical engineering, 2000-05, Vol.47 (5), p.594-599</ispartof><rights>2000 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2000</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c485t-617cdf7b5c4da9de4664e8e183f58dee62b23cd94a57ae34fb64a5d5206710e33</citedby><cites>FETCH-LOGICAL-c485t-617cdf7b5c4da9de4664e8e183f58dee62b23cd94a57ae34fb64a5d5206710e33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/841331$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1347512$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/10851803$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wubbeler, G.</creatorcontrib><creatorcontrib>Ziehe, A.</creatorcontrib><creatorcontrib>Mackert, B.-M.</creatorcontrib><creatorcontrib>Muller, K.-R.</creatorcontrib><creatorcontrib>Trahms, L.</creatorcontrib><creatorcontrib>Curio, C.</creatorcontrib><title>Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans</title><title>IEEE transactions on biomedical engineering</title><addtitle>TBME</addtitle><addtitle>IEEE Trans Biomed Eng</addtitle><description>We apply a recently developed multivariate statistical data analysis technique-so called blind source separation (BSS) by independent component analysis-to process magnetoencephalogram recordings of near-DC fields. The extraction of near-DC fields from MEG recordings has great relevance for medical applications since slowly varying DC-phenomena have been found, e.g., in cerebral anoxia and spreading depression in animals. Comparing several BSS approaches, it turns out that an algorithm based on temporal decorrelation successfully extracted a DC-component which was induced in the auditory cortex by presentation of music. The task is challenging because of the limited amount of available data and the corruption by outliers, which makes it an interesting real-world testbed for studying the robustness of ICA methods.</description><subject>Acoustic Stimulation</subject><subject>Algorithms</subject><subject>Auditory Cortex - physiology</subject><subject>Bioelectric potentials</subject><subject>Biological and medical sciences</subject><subject>Blind source separation</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Evoked Potentials, Auditory - physiology</subject><subject>Feature extraction</subject><subject>Humans</subject><subject>Independent component analysis</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Magnetic analysis</subject><subject>Magnetic recording</subject><subject>Magnetic separation</subject><subject>Magnetoencephalography</subject><subject>Medical sciences</subject><subject>Medical services</subject><subject>Nervous system</subject><subject>Neurophysiology</subject><subject>Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Source separation</subject><subject>Statistical methods</subject><issn>0018-9294</issn><issn>1558-2531</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><recordid>eNqN0UFrFTEQAOAgin2tHrx6kKVIi4fVzCbZnT2W11oLBS96dclLZm3KbvJM3hbevzfLPlR6UC-ZCfmYDDOMvQL-HoC3H3JECULAE7YCpbCslICnbMU5YNlWrTxixynd56tEWT9nR8BRAXKxYt9uvKUt5cPvChPGbfBzpr0e9smlIvSFD975B53cAw37IpIJ0ZLNOO6c0UMx6u-eclpcrsve0WBT4XxxN43apxfsWa-HRC8P8YR9_Xj1Zf2pvP18fbO-uC2NRLUra2iM7ZuNMtLq1pKsa0lIgKJXaInqalMJY1upVaNJyH5T59SqitcNcBLihJ0vdbcx_Jgo7brRJUPDoD2FKXWICCBQVFme_VU2AAhSNP-EVR45_y8IEmoh5iZPH8H7MMU86bk_VQmJHDN6tyATQ0qR-m4b3ajjvgPezdue47LtbN8cCk6bkewfcllvBm8PQKe8qj5qb1z67YRsFMwzeb0wR0S_Xg-f_ASdkbiD</recordid><startdate>20000501</startdate><enddate>20000501</enddate><creator>Wubbeler, G.</creator><creator>Ziehe, A.</creator><creator>Mackert, B.-M.</creator><creator>Muller, K.-R.</creator><creator>Trahms, L.</creator><creator>Curio, C.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><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>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20000501</creationdate><title>Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans</title><author>Wubbeler, G. ; Ziehe, A. ; Mackert, B.-M. ; Muller, K.-R. ; Trahms, L. ; Curio, C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c485t-617cdf7b5c4da9de4664e8e183f58dee62b23cd94a57ae34fb64a5d5206710e33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Acoustic Stimulation</topic><topic>Algorithms</topic><topic>Auditory Cortex - physiology</topic><topic>Bioelectric potentials</topic><topic>Biological and medical sciences</topic><topic>Blind source separation</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Evoked Potentials, Auditory - physiology</topic><topic>Feature extraction</topic><topic>Humans</topic><topic>Independent component analysis</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Magnetic analysis</topic><topic>Magnetic recording</topic><topic>Magnetic separation</topic><topic>Magnetoencephalography</topic><topic>Medical sciences</topic><topic>Medical services</topic><topic>Nervous system</topic><topic>Neurophysiology</topic><topic>Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Source separation</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wubbeler, G.</creatorcontrib><creatorcontrib>Ziehe, A.</creatorcontrib><creatorcontrib>Mackert, B.-M.</creatorcontrib><creatorcontrib>Muller, K.-R.</creatorcontrib><creatorcontrib>Trahms, L.</creatorcontrib><creatorcontrib>Curio, C.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE/IET Electronic Library (IEL)</collection><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>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on biomedical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wubbeler, G.</au><au>Ziehe, A.</au><au>Mackert, B.-M.</au><au>Muller, K.-R.</au><au>Trahms, L.</au><au>Curio, C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans</atitle><jtitle>IEEE transactions on biomedical engineering</jtitle><stitle>TBME</stitle><addtitle>IEEE Trans Biomed Eng</addtitle><date>2000-05-01</date><risdate>2000</risdate><volume>47</volume><issue>5</issue><spage>594</spage><epage>599</epage><pages>594-599</pages><issn>0018-9294</issn><eissn>1558-2531</eissn><coden>IEBEAX</coden><abstract>We apply a recently developed multivariate statistical data analysis technique-so called blind source separation (BSS) by independent component analysis-to process magnetoencephalogram recordings of near-DC fields. The extraction of near-DC fields from MEG recordings has great relevance for medical applications since slowly varying DC-phenomena have been found, e.g., in cerebral anoxia and spreading depression in animals. Comparing several BSS approaches, it turns out that an algorithm based on temporal decorrelation successfully extracted a DC-component which was induced in the auditory cortex by presentation of music. The task is challenging because of the limited amount of available data and the corruption by outliers, which makes it an interesting real-world testbed for studying the robustness of ICA methods.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>10851803</pmid><doi>10.1109/10.841331</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0018-9294 |
| ispartof | IEEE transactions on biomedical engineering, 2000-05, Vol.47 (5), p.594-599 |
| issn | 0018-9294 1558-2531 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_71181437 |
| source | IEEE Xplore (Online service) |
| subjects | Acoustic Stimulation Algorithms Auditory Cortex - physiology Bioelectric potentials Biological and medical sciences Blind source separation Data analysis Data mining Evoked Potentials, Auditory - physiology Feature extraction Humans Independent component analysis Investigative techniques, diagnostic techniques (general aspects) Magnetic analysis Magnetic recording Magnetic separation Magnetoencephalography Medical sciences Medical services Nervous system Neurophysiology Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques Signal Processing, Computer-Assisted Source separation Statistical methods |
| title | Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T20%3A26%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Independent%20component%20analysis%20of%20noninvasively%20recorded%20cortical%20magnetic%20DC-fields%20in%20humans&rft.jtitle=IEEE%20transactions%20on%20biomedical%20engineering&rft.au=Wubbeler,%20G.&rft.date=2000-05-01&rft.volume=47&rft.issue=5&rft.spage=594&rft.epage=599&rft.pages=594-599&rft.issn=0018-9294&rft.eissn=1558-2531&rft.coden=IEBEAX&rft_id=info:doi/10.1109/10.841331&rft_dat=%3Cproquest_pubme%3E2435166971%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c485t-617cdf7b5c4da9de4664e8e183f58dee62b23cd94a57ae34fb64a5d5206710e33%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=885234808&rft_id=info:pmid/10851803&rft_ieee_id=841331&rfr_iscdi=true |