Please use this identifier to cite or link to this item: https://doi.org/10.3389/fnins.2017.00325
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dc.titleA new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation
dc.contributor.authorBorghini, G
dc.contributor.authorAricò, P
dc.contributor.authorDi Flumeri, G
dc.contributor.authorSciaraffa, N
dc.contributor.authorColosimo, A
dc.contributor.authorHerrero, M.-T
dc.contributor.authorBezerianos, A
dc.contributor.authorThakor, N.V
dc.contributor.authorBabiloni, F
dc.date.accessioned2020-11-17T04:41:15Z
dc.date.available2020-11-17T04:41:15Z
dc.date.issued2017
dc.identifier.citationBorghini, G, Aricò, P, Di Flumeri, G, Sciaraffa, N, Colosimo, A, Herrero, M.-T, Bezerianos, A, Thakor, N.V, Babiloni, F (2017). A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation. Frontiers in Neuroscience 11 (JUN) : 325. ScholarBank@NUS Repository. https://doi.org/10.3389/fnins.2017.00325
dc.identifier.issn1662-4548
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/183518
dc.description.abstractInappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni.
dc.publisherFrontiers Media
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectbrain function
dc.subjectclinical article
dc.subjectelectroencephalogram
dc.subjectfemale
dc.subjecthuman
dc.subjectmachine learning
dc.subjectmale
dc.subjectquantitative study
dc.subjecttraining
dc.typeArticle
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.contributor.departmentLIFE SCIENCES INSTITUTE
dc.description.doi10.3389/fnins.2017.00325
dc.description.sourcetitleFrontiers in Neuroscience
dc.description.volume11
dc.description.issueJUN
dc.description.page325
dc.published.statePublished
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