On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task

Nicolina Sciaraffa, Pietro Aricò, Gianluca Borghini, Gianluca Di Flumeri, Antonio Di Florio, Fabio Babiloni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationHuman Mental Workload
Subtitle of host publicationModels and Applications - 3rd International Symposium, H-WORKLOAD 2019, Proceedings
EditorsLuca Longo, Maria Chiara Leva
PublisherSpringer
Pages170-185
Number of pages16
ISBN (Print)9783030324223
DOIs
Publication statusPublished - Jan 1 2019
Event3rd International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2019 - Rome, Italy
Duration: Nov 14 2019Nov 15 2019

Publication series

NameCommunications in Computer and Information Science
Volume1107
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2019
CountryItaly
CityRome
Period11/14/1911/15/19

Keywords

  • ATM
  • EEG
  • Machine learning
  • Real settings
  • Workload

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    Sciaraffa, N., Aricò, P., Borghini, G., Flumeri, G. D., Florio, A. D., & Babiloni, F. (2019). On the Use of Machine Learning for EEG-Based Workload Assessment: Algorithms Comparison in a Realistic Task. In L. Longo, & M. C. Leva (Eds.), Human Mental Workload: Models and Applications - 3rd International Symposium, H-WORKLOAD 2019, Proceedings (pp. 170-185). (Communications in Computer and Information Science; Vol. 1107). Springer. https://doi.org/10.1007/978-3-030-32423-0_11