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Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities
Abstract Objective Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. Methods We present a new met...
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| Published in: | Clinical neurophysiology 2008-04, Vol.119 (4), p.897-908 |
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| container_title | Clinical neurophysiology |
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| creator | Besserve, Michel Philippe, Matthieu Florence, Geneviève Laurent, François Garnero, Line Martinerie, Jacques |
| description | Abstract Objective Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. Methods We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject. Results The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49 bit/min. The main discriminating activities are laterally distributed theta power and anterio–posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance. Conclusions Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative. Significance Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes. |
| doi_str_mv | 10.1016/j.clinph.2007.12.003 |
| format | article |
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In this paper, the usefulness of EEG for the prediction of performance is investigated. Methods We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject. Results The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49 bit/min. The main discriminating activities are laterally distributed theta power and anterio–posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance. Conclusions Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative. Significance Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes.</description><identifier>ISSN: 1388-2457</identifier><identifier>EISSN: 1872-8952</identifier><identifier>DOI: 10.1016/j.clinph.2007.12.003</identifier><identifier>PMID: 18296110</identifier><language>eng</language><publisher>Shannon: Elsevier Ireland Ltd</publisher><subject>Algorithms ; Behavioral psychophysiology ; Biological and medical sciences ; Brain - physiology ; Brain Mapping ; Classification ; Cognition - physiology ; Computer Science ; Electrodiagnosis. Electric activity recording ; Electroencephalography ; Electrophysiology ; Fundamental and applied biological sciences. Psychology ; Humans ; Investigative techniques, diagnostic techniques (general aspects) ; Medical Imaging ; Medical sciences ; Nervous system ; Neurology ; Phase locking value ; Phase synchrony ; Psychology. Psychoanalysis. Psychiatry ; Psychology. Psychophysiology ; Support vector machine ; Task Performance and Analysis</subject><ispartof>Clinical neurophysiology, 2008-04, Vol.119 (4), p.897-908</ispartof><rights>International Federation of Clinical Neurophysiology</rights><rights>2007 International Federation of Clinical Neurophysiology</rights><rights>2008 INIST-CNRS</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c479t-29cf81b4e03f3f2f907f57216fea9df2f2c33458254020bdeff57459d2fad47d3</citedby><cites>FETCH-LOGICAL-c479t-29cf81b4e03f3f2f907f57216fea9df2f2c33458254020bdeff57459d2fad47d3</cites><orcidid>0000-0002-1615-6400</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,786,790,891,27957,27958</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20214241$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18296110$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://inria.hal.science/hal-00805436$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Besserve, Michel</creatorcontrib><creatorcontrib>Philippe, Matthieu</creatorcontrib><creatorcontrib>Florence, Geneviève</creatorcontrib><creatorcontrib>Laurent, François</creatorcontrib><creatorcontrib>Garnero, Line</creatorcontrib><creatorcontrib>Martinerie, Jacques</creatorcontrib><title>Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities</title><title>Clinical neurophysiology</title><addtitle>Clin Neurophysiol</addtitle><description>Abstract Objective Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. Methods We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject. Results The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49 bit/min. The main discriminating activities are laterally distributed theta power and anterio–posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance. Conclusions Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative. Significance Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes.</description><subject>Algorithms</subject><subject>Behavioral psychophysiology</subject><subject>Biological and medical sciences</subject><subject>Brain - physiology</subject><subject>Brain Mapping</subject><subject>Classification</subject><subject>Cognition - physiology</subject><subject>Computer Science</subject><subject>Electrodiagnosis. Electric activity recording</subject><subject>Electroencephalography</subject><subject>Electrophysiology</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Humans</subject><subject>Investigative techniques, diagnostic techniques (general aspects)</subject><subject>Medical Imaging</subject><subject>Medical sciences</subject><subject>Nervous system</subject><subject>Neurology</subject><subject>Phase locking value</subject><subject>Phase synchrony</subject><subject>Psychology. Psychoanalysis. Psychiatry</subject><subject>Psychology. Psychophysiology</subject><subject>Support vector machine</subject><subject>Task Performance and Analysis</subject><issn>1388-2457</issn><issn>1872-8952</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNqFkl2L1DAUhoso7of-A5HcKHjRmq-26Y2wLOOuMKCgXodMejKb2bYZk7Yw_95TOuyCN14lJM95z-F9T5a9Y7RglFWfD4Xt_HB8KDildcF4Qal4kV0yVfNcNSV_iXehVM5lWV9kVykdKIJU8tfZBVO8qRijl5n-EaH1dvRhIMGRI0QXYm8GC6SDGTrSTtEPe2KIDfvBj34GMpr0SFwMPQnDPiy_m80dCcn6rjNjiCdiUHBGGNKb7JUzXYK35_M6-_118-v2Pt9-v_t2e7PNraybMeeNdYrtJFDhhOOuobUra84qB6Zp8YFbIWSpeCkpp7sWHH7Lsmm5M62sW3GdfVp1H0ynj9H3Jp50MF7f32z18kapoqUU1cyQ_biyxxj-TJBG3ftkAYcfIExJo0nopaoQlCtoY0gpgntSZlQvIeiDXkPQSwiacWwjsOz9WX_a9dA-F51dR-DDGTDJms5F9NunJ45TziSXy6BfVg7QudlD1OgxYDatj2BH3Qb_v0n-FVggjz0f4QTpEKY4YCqa6YQF-ueyMMu-4J5Q1dRK_AW7Zbvd</recordid><startdate>20080401</startdate><enddate>20080401</enddate><creator>Besserve, Michel</creator><creator>Philippe, Matthieu</creator><creator>Florence, Geneviève</creator><creator>Laurent, François</creator><creator>Garnero, Line</creator><creator>Martinerie, Jacques</creator><general>Elsevier Ireland Ltd</general><general>Elsevier Science</general><general>Elsevier</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><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-1615-6400</orcidid></search><sort><creationdate>20080401</creationdate><title>Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities</title><author>Besserve, Michel ; Philippe, Matthieu ; Florence, Geneviève ; Laurent, François ; Garnero, Line ; Martinerie, Jacques</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c479t-29cf81b4e03f3f2f907f57216fea9df2f2c33458254020bdeff57459d2fad47d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Algorithms</topic><topic>Behavioral psychophysiology</topic><topic>Biological and medical sciences</topic><topic>Brain - physiology</topic><topic>Brain Mapping</topic><topic>Classification</topic><topic>Cognition - physiology</topic><topic>Computer Science</topic><topic>Electrodiagnosis. Electric activity recording</topic><topic>Electroencephalography</topic><topic>Electrophysiology</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Humans</topic><topic>Investigative techniques, diagnostic techniques (general aspects)</topic><topic>Medical Imaging</topic><topic>Medical sciences</topic><topic>Nervous system</topic><topic>Neurology</topic><topic>Phase locking value</topic><topic>Phase synchrony</topic><topic>Psychology. Psychoanalysis. Psychiatry</topic><topic>Psychology. Psychophysiology</topic><topic>Support vector machine</topic><topic>Task Performance and Analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Besserve, Michel</creatorcontrib><creatorcontrib>Philippe, Matthieu</creatorcontrib><creatorcontrib>Florence, Geneviève</creatorcontrib><creatorcontrib>Laurent, François</creatorcontrib><creatorcontrib>Garnero, Line</creatorcontrib><creatorcontrib>Martinerie, Jacques</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><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Clinical neurophysiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Besserve, Michel</au><au>Philippe, Matthieu</au><au>Florence, Geneviève</au><au>Laurent, François</au><au>Garnero, Line</au><au>Martinerie, Jacques</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities</atitle><jtitle>Clinical neurophysiology</jtitle><addtitle>Clin Neurophysiol</addtitle><date>2008-04-01</date><risdate>2008</risdate><volume>119</volume><issue>4</issue><spage>897</spage><epage>908</epage><pages>897-908</pages><issn>1388-2457</issn><eissn>1872-8952</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>Abstract Objective Tracking the level of performance in cognitive tasks may be useful in environments, such as aircraft, in which the awareness of the pilots is critical for security. In this paper, the usefulness of EEG for the prediction of performance is investigated. Methods We present a new methodology that combines various ongoing EEG measurements to predict performance level during a cognitive task. We propose a voting approach that combines the outputs of elementary support vector machine (SVM) classifiers derived from various sets of EEG parameters in different frequency bands. The spectral power and phase synchrony of the oscillatory activities are used to classify the periods of rapid reaction time (RT) versus the slow RT responses of each subject. Results The voting algorithm significantly outperforms classical SVM and gives a good average classification accuracy across 12 subjects (71%) and an average information transfer rate (ITR) of 0.49 bit/min. The main discriminating activities are laterally distributed theta power and anterio–posterior alpha synchronies, possibly reflecting the role of a visual-attentional network in performance. Conclusions Power and synchrony measurements enable the discrimination between periods of high average reaction time versus periods of low average reaction time in a same subject. Moreover, the proposed approach is easy to interpret as it combines various types of measurements for classification, emphasizing the most informative. Significance Ongoing EEG recordings can predict the level of performance during a cognitive task. This can lead to real-time EEG monitoring devices for the anticipation of human mistakes.</abstract><cop>Shannon</cop><pub>Elsevier Ireland Ltd</pub><pmid>18296110</pmid><doi>10.1016/j.clinph.2007.12.003</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-1615-6400</orcidid></addata></record> |
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| ispartof | Clinical neurophysiology, 2008-04, Vol.119 (4), p.897-908 |
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| subjects | Algorithms Behavioral psychophysiology Biological and medical sciences Brain - physiology Brain Mapping Classification Cognition - physiology Computer Science Electrodiagnosis. Electric activity recording Electroencephalography Electrophysiology Fundamental and applied biological sciences. Psychology Humans Investigative techniques, diagnostic techniques (general aspects) Medical Imaging Medical sciences Nervous system Neurology Phase locking value Phase synchrony Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Support vector machine Task Performance and Analysis |
| title | Prediction of performance level during a cognitive task from ongoing EEG oscillatory activities |
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