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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. | 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. | ||
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* [https://iris.uniroma1.it/bitstream/11573/1341093/1/Sciaraffa_Postprint_On-the-Use%20of-Machine_2019.pdf https://iris.uniroma1.it/bitstream/11573/1341093/1/Sciaraffa_Postprint_On-the-Use%20of-Machine_2019.pdf] | * [https://iris.uniroma1.it/bitstream/11573/1341093/1/Sciaraffa_Postprint_On-the-Use%20of-Machine_2019.pdf 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://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 http://dx.doi.org/10.1007/978-3-030-32423-0_11] under the license http://www.springer.com/tdm | + | * [http://link.springer.com/content/pdf/10.1007/978-3-030-32423-0_11 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 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://link.springer.com/chapter/10.1007%2F978-3-030-32423-0_11],[https://academic.microsoft.com/#/detail/2980154133 https://academic.microsoft.com/#/detail/2980154133] | + | * [https://link.springer.com/chapter/10.1007%2F978-3-030-32423-0_11 https://link.springer.com/chapter/10.1007%2F978-3-030-32423-0_11], |
| + | : [https://www.scipedia.com/public/Sciaraffa_et_al_2019a https://www.scipedia.com/public/Sciaraffa_et_al_2019a], | ||
| + | : [https://dblp.uni-trier.de/db/conf/hworkload/hworkload2019.html#SciaraffaABFFB19 https://dblp.uni-trier.de/db/conf/hworkload/hworkload2019.html#SciaraffaABFFB19], | ||
| + | : [https://academic.microsoft.com/#/detail/2980154133 https://academic.microsoft.com/#/detail/2980154133] | ||
Latest revision as of 17:11, 21 January 2021
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
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
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