Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes
This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness pe...
Gespeichert in:
| Veröffentlicht in: | 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008-01, Vol.2008, p.5871-5875 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5875 |
|---|---|
| container_issue | |
| container_start_page | 5871 |
| container_title | 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
| container_volume | 2008 |
| creator | Matthews, R. Turner, P.J. McDonald, N. J. Ermolaev, K. Manus, T. Mc Shelby, R.A. Steindorf, M. |
| description | This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload. |
| doi_str_mv | 10.1109/IEMBS.2008.4650550 |
| format | Article |
| fullrecord | <record><control><sourceid>pubmed_6IE</sourceid><recordid>TN_cdi_ieee_primary_4650550</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4650550</ieee_id><sourcerecordid>19164053</sourcerecordid><originalsourceid>FETCH-LOGICAL-c414t-9136de6e51effd4a18d8ac8fda177b1c2bbaa0785c3eb321e99496779ad7b8403</originalsourceid><addsrcrecordid>eNpVkF1LwzAUhuMXbsz9AQXJH-jMaZMmudQx52Ai-AHejaQ51Wi7StIx-u8tbgqemwPneXjhPYScA5sAMH21mN3fPE1SxtSE54IJwQ7IWEsFPOUcFIj0kAxBCJXwHMTRP8bFcc-Y5kmu5OuAjGP8YP1wkeWKnZIBaMg5E9mQuEc0FW19jXTbhM-qMY4WlYnRl74wrW_WtAxNTc2amtpuKtM2oaNbH7DCGOlsNqexiy3WdBP9-o2-dzZ493PvjaINjcN4Rk5KU0Uc7_eIvNzOnqd3yfJhvpheL5OCA28TDVnuMEcBWJaOG1BOmUKVzoCUForUWmOYVKLI0GYpoNZc51Jq46RVnGUjcrnL_drYGt3qK_jahG71W7cXLnaCR8Q_vP9v9g2lLWiD</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Matthews, R. ; Turner, P.J. ; McDonald, N. J. ; Ermolaev, K. ; Manus, T. Mc ; Shelby, R.A. ; Steindorf, M.</creator><creatorcontrib>Matthews, R. ; Turner, P.J. ; McDonald, N. J. ; Ermolaev, K. ; Manus, T. Mc ; Shelby, R.A. ; Steindorf, M.</creatorcontrib><description>This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.</description><identifier>ISSN: 1094-687X</identifier><identifier>ISSN: 1557-170X</identifier><identifier>ISBN: 9781424418145</identifier><identifier>ISBN: 1424418143</identifier><identifier>EISSN: 1558-4615</identifier><identifier>EISBN: 9781424418152</identifier><identifier>EISBN: 1424418151</identifier><identifier>DOI: 10.1109/IEMBS.2008.4650550</identifier><identifier>PMID: 19164053</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Brain - physiology ; Electrodes ; Electroencephalography - instrumentation ; Electroencephalography - methods ; Equipment Design ; Equipment Failure Analysis ; Monitoring, Ambulatory - instrumentation ; Monitoring, Ambulatory - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Telemetry - instrumentation ; Workload</subject><ispartof>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008-01, Vol.2008, p.5871-5875</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-9136de6e51effd4a18d8ac8fda177b1c2bbaa0785c3eb321e99496779ad7b8403</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4650550$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4650550$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19164053$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Matthews, R.</creatorcontrib><creatorcontrib>Turner, P.J.</creatorcontrib><creatorcontrib>McDonald, N. J.</creatorcontrib><creatorcontrib>Ermolaev, K.</creatorcontrib><creatorcontrib>Manus, T. Mc</creatorcontrib><creatorcontrib>Shelby, R.A.</creatorcontrib><creatorcontrib>Steindorf, M.</creatorcontrib><title>Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes</title><title>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</title><addtitle>IEMBS</addtitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><description>This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.</description><subject>Algorithms</subject><subject>Brain - physiology</subject><subject>Electrodes</subject><subject>Electroencephalography - instrumentation</subject><subject>Electroencephalography - methods</subject><subject>Equipment Design</subject><subject>Equipment Failure Analysis</subject><subject>Monitoring, Ambulatory - instrumentation</subject><subject>Monitoring, Ambulatory - methods</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Telemetry - instrumentation</subject><subject>Workload</subject><issn>1094-687X</issn><issn>1557-170X</issn><issn>1558-4615</issn><isbn>9781424418145</isbn><isbn>1424418143</isbn><isbn>9781424418152</isbn><isbn>1424418151</isbn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpVkF1LwzAUhuMXbsz9AQXJH-jMaZMmudQx52Ai-AHejaQ51Wi7StIx-u8tbgqemwPneXjhPYScA5sAMH21mN3fPE1SxtSE54IJwQ7IWEsFPOUcFIj0kAxBCJXwHMTRP8bFcc-Y5kmu5OuAjGP8YP1wkeWKnZIBaMg5E9mQuEc0FW19jXTbhM-qMY4WlYnRl74wrW_WtAxNTc2amtpuKtM2oaNbH7DCGOlsNqexiy3WdBP9-o2-dzZ493PvjaINjcN4Rk5KU0Uc7_eIvNzOnqd3yfJhvpheL5OCA28TDVnuMEcBWJaOG1BOmUKVzoCUForUWmOYVKLI0GYpoNZc51Jq46RVnGUjcrnL_drYGt3qK_jahG71W7cXLnaCR8Q_vP9v9g2lLWiD</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Matthews, R.</creator><creator>Turner, P.J.</creator><creator>McDonald, N. J.</creator><creator>Ermolaev, K.</creator><creator>Manus, T. Mc</creator><creator>Shelby, R.A.</creator><creator>Steindorf, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope></search><sort><creationdate>20080101</creationdate><title>Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes</title><author>Matthews, R. ; Turner, P.J. ; McDonald, N. J. ; Ermolaev, K. ; Manus, T. Mc ; Shelby, R.A. ; Steindorf, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c414t-9136de6e51effd4a18d8ac8fda177b1c2bbaa0785c3eb321e99496779ad7b8403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Algorithms</topic><topic>Brain - physiology</topic><topic>Electrodes</topic><topic>Electroencephalography - instrumentation</topic><topic>Electroencephalography - methods</topic><topic>Equipment Design</topic><topic>Equipment Failure Analysis</topic><topic>Monitoring, Ambulatory - instrumentation</topic><topic>Monitoring, Ambulatory - methods</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Telemetry - instrumentation</topic><topic>Workload</topic><toplevel>online_resources</toplevel><creatorcontrib>Matthews, R.</creatorcontrib><creatorcontrib>Turner, P.J.</creatorcontrib><creatorcontrib>McDonald, N. J.</creatorcontrib><creatorcontrib>Ermolaev, K.</creatorcontrib><creatorcontrib>Manus, T. Mc</creatorcontrib><creatorcontrib>Shelby, R.A.</creatorcontrib><creatorcontrib>Steindorf, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><jtitle>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Matthews, R.</au><au>Turner, P.J.</au><au>McDonald, N. J.</au><au>Ermolaev, K.</au><au>Manus, T. Mc</au><au>Shelby, R.A.</au><au>Steindorf, M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes</atitle><jtitle>2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</jtitle><stitle>IEMBS</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2008-01-01</date><risdate>2008</risdate><volume>2008</volume><spage>5871</spage><epage>5875</epage><pages>5871-5875</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><isbn>9781424418145</isbn><isbn>1424418143</isbn><eisbn>9781424418152</eisbn><eisbn>1424418151</eisbn><abstract>This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>19164053</pmid><doi>10.1109/IEMBS.2008.4650550</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1094-687X |
| ispartof | 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008-01, Vol.2008, p.5871-5875 |
| issn | 1094-687X 1557-170X 1558-4615 |
| language | eng |
| recordid | cdi_ieee_primary_4650550 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithms Brain - physiology Electrodes Electroencephalography - instrumentation Electroencephalography - methods Equipment Design Equipment Failure Analysis Monitoring, Ambulatory - instrumentation Monitoring, Ambulatory - methods Reproducibility of Results Sensitivity and Specificity Telemetry - instrumentation Workload |
| title | Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T21%3A29%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pubmed_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Real%20time%20workload%20classification%20from%20an%20ambulatory%20wireless%20EEG%20system%20using%20hybrid%20EEG%20electrodes&rft.jtitle=2008%2030th%20Annual%20International%20Conference%20of%20the%20IEEE%20Engineering%20in%20Medicine%20and%20Biology%20Society&rft.au=Matthews,%20R.&rft.date=2008-01-01&rft.volume=2008&rft.spage=5871&rft.epage=5875&rft.pages=5871-5875&rft.issn=1094-687X&rft.eissn=1558-4615&rft.isbn=9781424418145&rft.isbn_list=1424418143&rft_id=info:doi/10.1109/IEMBS.2008.4650550&rft_dat=%3Cpubmed_6IE%3E19164053%3C/pubmed_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424418152&rft.eisbn_list=1424418151&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/19164053&rft_ieee_id=4650550&rfr_iscdi=true |