TY - JOUR
T1 - A new perspective for the training assessment
T2 - Machine learning-based neurometric for augmented user's evaluation
AU - Borghini, Gianluca
AU - Aricò, Pietro
AU - Di Flumeri, Gianluca
AU - Sciaraffa, Nicolina
AU - Colosimo, Alfredo
AU - Herrero, Maria Trinidad
AU - Bezerianos, Anastasios
AU - Thakor, Nitish V.
AU - Babiloni, Fabio
N1 - Publisher Copyright:
© 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni.
PY - 2017/6/13
Y1 - 2017/6/13
N2 - 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.
AB - 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.
KW - Brain activity
KW - EEG
KW - Human factor
KW - Human machine interaction
KW - Machine learning
KW - Training assessment
UR - http://www.scopus.com/inward/record.url?scp=85020818202&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020818202&partnerID=8YFLogxK
U2 - 10.3389/fnins.2017.00325
DO - 10.3389/fnins.2017.00325
M3 - Article
C2 - 28659751
AN - SCOPUS:85020818202
SN - 1662-4548
VL - 11
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
IS - JUN
M1 - 325
ER -