Please use this identifier to cite or link to this item: https://doi.org/10.3389/fnins.2017.00325
Title: A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation
Authors: Borghini, G
Aricò, P
Di Flumeri, G
Sciaraffa, N
Colosimo, A
Herrero, M.-T
Bezerianos, A 
Thakor, N.V 
Babiloni, F
Keywords: brain function
clinical article
electroencephalogram
female
human
machine learning
male
quantitative study
training
Issue Date: 2017
Publisher: Frontiers Media
Citation: Borghini, 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
Rights: Attribution 4.0 International
Abstract: Inappropriate 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.
Source Title: Frontiers in Neuroscience
URI: https://scholarbank.nus.edu.sg/handle/10635/183518
ISSN: 1662-4548
DOI: 10.3389/fnins.2017.00325
Rights: Attribution 4.0 International
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