On the use of machine learning for eeg based workload assessment algorithms comparison in a realistic task

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.

Document type: Part of book or chapter of book

Original document

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

• http://hdl.handle.net/11573/1341093

• https://iris.uniroma1.it/bitstream/11573/1341093/1/Sciaraffa_Postprint_On-the-Use%20of-Machine_2019.pdf

• http://link.springer.com/content/pdf/10.1007/978-3-030-32423-0_11,http://dx.doi.org/10.1007/978-3-030-32423-0_11 under the license http://www.springer.com/tdm

• https://link.springer.com/chapter/10.1007%2F978-3-030-32423-0_11,https://academic.microsoft.com/#/detail/2980154133