In this study, a framework able to classify online different levels of mental workload induced during a simulated flight by using the combination of the Electroencephalogram (EEG) and the Heart Rate (HR) biosignals has been proposed. Ten healthy subjects were involved in the experimental protocol, performing the NASA - Multi Attribute Task Battery (MATB) over three different difficulty levels in order to simulate three classic showcases in a flight scene (cruise flight phase, flight level maintaining, and emergencies). The analyses showed that the proposed system is able to estimate online the mental workload of the subjects over the three different conditions reaching a high discriminability (p<;.05). In addition, it has been found that the classification parameters remained stable within a week, without recalibrating the system with new parameters.
Towards a multimodal bioelectrical framework for the online mental workload evaluation / Aricò, Pietro; Borghini, Gianluca; Graziani, Ilenia; Taya, Fumihico; Sun, Yu; Bezerianos, Anastasios; Thakor, Nitish V.; Cincotti, Febo; Babiloni, Fabio. - ELETTRONICO. - 2014:(2014), pp. 3001-3004. (Intervento presentato al convegno Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference nel 2014-Aug) [10.1109/EMBC.2014.6944254].
Towards a multimodal bioelectrical framework for the online mental workload evaluation
Aricò, Pietro;BORGHINI, GIANLUCA;GRAZIANI, ILENIA;CINCOTTI, FEBO;BABILONI, Fabio
2014
Abstract
In this study, a framework able to classify online different levels of mental workload induced during a simulated flight by using the combination of the Electroencephalogram (EEG) and the Heart Rate (HR) biosignals has been proposed. Ten healthy subjects were involved in the experimental protocol, performing the NASA - Multi Attribute Task Battery (MATB) over three different difficulty levels in order to simulate three classic showcases in a flight scene (cruise flight phase, flight level maintaining, and emergencies). The analyses showed that the proposed system is able to estimate online the mental workload of the subjects over the three different conditions reaching a high discriminability (p<;.05). In addition, it has been found that the classification parameters remained stable within a week, without recalibrating the system with new parameters.File | Dimensione | Formato | |
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