Abstract

The measurement of the mental workload during real tasks by means of neurophysiological signals is still challenging. The employment of Machine Learning techniques has allowed a step forward in this direction, however, most of the work has dealt with binary classification. This study proposed to examine the surveys already performed in the context of EEG-based workload classification and to test different machine learning algorithms on real multitasking activity like the Air Traffic Management. The results obtained on 35 professional Air Traffic Controllers showed that a KNN algorithm allows discriminating up to three workload levels (low, medium and high) with more than 84% of accuracy on average. Moreover, in such realistic employment it emerges how important is to opportunely choose the set of features to ward off that task-related confounds could affect the workload assessment.


Original document

The different versions of the original document can be found in:

http://dx.doi.org/10.1007/978-3-030-32423-0_11 under the license http://www.springer.com/tdm
https://www.scipedia.com/public/Sciaraffa_et_al_2019a,
https://dblp.uni-trier.de/db/conf/hworkload/hworkload2019.html#SciaraffaABFFB19,
https://academic.microsoft.com/#/detail/2980154133
Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.1007/978-3-030-32423-0_11
Licence: CC BY-NC-SA license

Document Score

0

Views 3
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?