A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks.
Identifieur interne : 000F85 ( Main/Exploration ); précédent : 000F84; suivant : 000F86A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks.
Auteurs : P. Aric [Italie] ; G. Borghini [Italie] ; G. Di Flumeri [Italie] ; A. Colosimo [Italie] ; S. Pozzi [Italie] ; F. Babiloni [Italie]Source :
- Progress in brain research [ 1875-7855 ] ; 2016.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Adulte d'âge moyen (MeSH), Analyse de variance (MeSH), Analyse et exécution des tâches (MeSH), Auto-évaluation (psychologie) (MeSH), Aviation (MeSH), Charge de travail (psychologie), Détection du signal (psychologie) (MeSH), Encéphale (physiologie), Facteurs temps (MeSH), Humains (MeSH), Interfaces cerveau-ordinateur (MeSH), Mâle (MeSH), Performance psychomotrice (physiologie), Reproductibilité des résultats (MeSH), Tests neuropsychologiques (MeSH), Électroencéphalographie (MeSH).
- MESH :
- physiologie : Encéphale, Performance psychomotrice.
- psychologie : Charge de travail.
- Adulte, Adulte d'âge moyen, Analyse de variance, Analyse et exécution des tâches, Auto-évaluation (psychologie), Aviation, Détection du signal (psychologie), Facteurs temps, Humains, Interfaces cerveau-ordinateur, Mâle, Reproductibilité des résultats, Tests neuropsychologiques, Électroencéphalographie.
English descriptors
- KwdEn :
- Adult (MeSH), Analysis of Variance (MeSH), Aviation (MeSH), Brain (physiology), Brain-Computer Interfaces (MeSH), Electroencephalography (MeSH), Humans (MeSH), Male (MeSH), Middle Aged (MeSH), Neuropsychological Tests (MeSH), Psychomotor Performance (physiology), Reproducibility of Results (MeSH), Self-Assessment (MeSH), Signal Detection, Psychological (MeSH), Task Performance and Analysis (MeSH), Time Factors (MeSH), Workload (psychology).
- MESH :
- physiology : Brain, Psychomotor Performance.
- psychology : Workload.
- Adult, Analysis of Variance, Aviation, Brain-Computer Interfaces, Electroencephalography, Humans, Male, Middle Aged, Neuropsychological Tests, Reproducibility of Results, Self-Assessment, Signal Detection, Psychological, Task Performance and Analysis, Time Factors.
Abstract
In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.
DOI: 10.1016/bs.pbr.2016.04.021
PubMed: 27590973
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Aric</name><affiliation wicri:level="3"><nlm:affiliation>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome</wicri:regionArea><placeName><settlement type="city">Rome</settlement><region nuts="2">Latium</region></placeName></affiliation></author><author><name sortKey="Borghini, G" sort="Borghini, G" uniqKey="Borghini G" first="G" last="Borghini">G. Borghini</name><affiliation wicri:level="3"><nlm:affiliation>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome</wicri:regionArea><placeName><settlement type="city">Rome</settlement><region nuts="2">Latium</region></placeName></affiliation></author><author><name sortKey="Di Flumeri, G" sort="Di Flumeri, G" uniqKey="Di Flumeri G" first="G" last="Di Flumeri">G. 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Colosimo</name><affiliation wicri:level="3"><nlm:affiliation>University of Rome "Sapienza", Rome, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>University of Rome "Sapienza", Rome</wicri:regionArea><placeName><settlement type="city">Rome</settlement><region nuts="2">Latium</region></placeName></affiliation></author><author><name sortKey="Pozzi, S" sort="Pozzi, S" uniqKey="Pozzi S" first="S" last="Pozzi">S. Pozzi</name><affiliation wicri:level="3"><nlm:affiliation>DeepBlue srl, Rome, Italy.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>DeepBlue srl, Rome</wicri:regionArea><placeName><settlement type="city">Rome</settlement><region nuts="2">Latium</region></placeName></affiliation></author><author><name sortKey="Babiloni, F" sort="Babiloni, F" uniqKey="Babiloni F" first="F" last="Babiloni">F. Babiloni</name><affiliation wicri:level="3"><nlm:affiliation>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy. Electronic address: fabio.babiloni@uniroma1.it.</nlm:affiliation><country xml:lang="fr">Italie</country><wicri:regionArea>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome</wicri:regionArea><placeName><settlement type="city">Rome</settlement><region nuts="2">Latium</region></placeName></affiliation></author></analytic><series><title level="j">Progress in brain research</title><idno type="eISSN">1875-7855</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Analysis of Variance (MeSH)</term><term>Aviation (MeSH)</term><term>Brain (physiology)</term><term>Brain-Computer Interfaces (MeSH)</term><term>Electroencephalography (MeSH)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Neuropsychological Tests (MeSH)</term><term>Psychomotor Performance (physiology)</term><term>Reproducibility of Results (MeSH)</term><term>Self-Assessment (MeSH)</term><term>Signal Detection, Psychological (MeSH)</term><term>Task Performance and Analysis (MeSH)</term><term>Time Factors (MeSH)</term><term>Workload (psychology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Analyse de variance (MeSH)</term><term>Analyse et exécution des tâches (MeSH)</term><term>Auto-évaluation (psychologie) (MeSH)</term><term>Aviation (MeSH)</term><term>Charge de travail (psychologie)</term><term>Détection du signal (psychologie) (MeSH)</term><term>Encéphale (physiologie)</term><term>Facteurs temps (MeSH)</term><term>Humains (MeSH)</term><term>Interfaces cerveau-ordinateur (MeSH)</term><term>Mâle (MeSH)</term><term>Performance psychomotrice (physiologie)</term><term>Reproductibilité des résultats (MeSH)</term><term>Tests neuropsychologiques (MeSH)</term><term>Électroencéphalographie (MeSH)</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Encéphale</term><term>Performance psychomotrice</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Brain</term><term>Psychomotor Performance</term></keywords><keywords scheme="MESH" qualifier="psychologie" xml:lang="fr"><term>Charge de travail</term></keywords><keywords scheme="MESH" qualifier="psychology" xml:lang="en"><term>Workload</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Analysis of Variance</term><term>Aviation</term><term>Brain-Computer Interfaces</term><term>Electroencephalography</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Neuropsychological Tests</term><term>Reproducibility of Results</term><term>Self-Assessment</term><term>Signal Detection, Psychological</term><term>Task Performance and Analysis</term><term>Time Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Analyse de variance</term><term>Analyse et exécution des tâches</term><term>Auto-évaluation (psychologie)</term><term>Aviation</term><term>Détection du signal (psychologie)</term><term>Facteurs temps</term><term>Humains</term><term>Interfaces cerveau-ordinateur</term><term>Mâle</term><term>Reproductibilité des résultats</term><term>Tests neuropsychologiques</term><term>Électroencéphalographie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">27590973</PMID><DateCompleted><Year>2017</Year><Month>06</Month><Day>22</Day></DateCompleted><DateRevised><Year>2018</Year><Month>06</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1875-7855</ISSN><JournalIssue CitedMedium="Internet"><Volume>228</Volume><PubDate><Year>2016</Year></PubDate></JournalIssue><Title>Progress in brain research</Title><ISOAbbreviation>Prog Brain Res</ISOAbbreviation></Journal><ArticleTitle>A passive brain-computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks.</ArticleTitle><Pagination><MedlinePgn>295-328</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/bs.pbr.2016.04.021</ELocationID><ELocationID EIdType="pii" ValidYN="Y">S0079-6123(16)30047-4</ELocationID><Abstract><AbstractText>In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the École Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.</AbstractText><CopyrightInformation>© 2016 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Aricò</LastName><ForeName>P</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Borghini</LastName><ForeName>G</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Di Flumeri</LastName><ForeName>G</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy; Neuroelectrical Imaging and BCI Lab, IRCCS Fondazione Santa Lucia, Rome, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Colosimo</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>University of Rome "Sapienza", Rome, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pozzi</LastName><ForeName>S</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>DeepBlue srl, Rome, Italy.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Babiloni</LastName><ForeName>F</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>University of Rome "Sapienza", Rome, Italy; BrainSigns srl, Rome, Italy. Electronic address: fabio.babiloni@uniroma1.it.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>06</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Prog Brain Res</MedlineTA><NlmUniqueID>0376441</NlmUniqueID><ISSNLinking>0079-6123</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000704" MajorTopicYN="N">Analysis of Variance</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001359" MajorTopicYN="Y">Aviation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001921" MajorTopicYN="N">Brain</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D062207" MajorTopicYN="Y">Brain-Computer Interfaces</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004569" MajorTopicYN="N">Electroencephalography</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009483" MajorTopicYN="N">Neuropsychological Tests</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011597" MajorTopicYN="N">Psychomotor Performance</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012647" MajorTopicYN="N">Self-Assessment</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017603" MajorTopicYN="N">Signal Detection, Psychological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013647" MajorTopicYN="Y">Task Performance and Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016526" MajorTopicYN="N">Workload</DescriptorName><QualifierName UI="Q000523" MajorTopicYN="Y">psychology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Air traffic management</Keyword><Keyword MajorTopicYN="Y">Augmented cognition</Keyword><Keyword MajorTopicYN="Y">Automatic-stop stepwise linear discriminant analysis</Keyword><Keyword MajorTopicYN="Y">Electroencephalogram</Keyword><Keyword MajorTopicYN="Y">Human factor</Keyword><Keyword MajorTopicYN="Y">Instantaneous self-assessment</Keyword><Keyword MajorTopicYN="Y">Mental workload</Keyword><Keyword MajorTopicYN="Y">Neuroergonomic</Keyword><Keyword MajorTopicYN="Y">Passive brain–computer interface</Keyword><Keyword MajorTopicYN="Y">Stepwise linear discriminant analysis</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>9</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>9</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>6</Month><Day>24</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">27590973</ArticleId><ArticleId IdType="pii">S0079-6123(16)30047-4</ArticleId><ArticleId IdType="doi">10.1016/bs.pbr.2016.04.021</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Italie</li></country><region><li>Latium</li></region><settlement><li>Rome</li></settlement></list><tree><country name="Italie"><region name="Latium"><name sortKey="Aric, P" sort="Aric, P" uniqKey="Aric P" first="P" last="Aric">P. Aric</name></region><name sortKey="Babiloni, F" sort="Babiloni, F" uniqKey="Babiloni F" first="F" last="Babiloni">F. Babiloni</name><name sortKey="Borghini, G" sort="Borghini, G" uniqKey="Borghini G" first="G" last="Borghini">G. Borghini</name><name sortKey="Colosimo, A" sort="Colosimo, A" uniqKey="Colosimo A" first="A" last="Colosimo">A. Colosimo</name><name sortKey="Di Flumeri, G" sort="Di Flumeri, G" uniqKey="Di Flumeri G" first="G" last="Di Flumeri">G. Di Flumeri</name><name sortKey="Pozzi, S" sort="Pozzi, S" uniqKey="Pozzi S" first="S" last="Pozzi">S. Pozzi</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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