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A Novel Mutual Information based Feature Set for Drivers’ Mental Workload Evaluation using Machine Learning

Authors:

Mir Riyanul Islam, Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum, Pietro Aricò , Gianluca Borghini , Gianluca Di Flumeri

Publication Type:

Journal article

Venue:

Brain Sciences (Special Issue: Brain Plasticity, Cognitive Training and Mental States Assessment)


Abstract

Analysis of physiological signals, electroencephalography in more specific notion, is considered as a very promising technique to obtain objective measures for mental workload evaluation, however, it requires complex apparatus to record and thus with poor usability in monitoring in-vehicle drivers’mental workload. This study proposes amethodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.

Bibtex

@article{Islam5877,
author = {Mir Riyanul Islam and Shaibal Barua and Mobyen Uddin Ahmed and Shahina Begum and Pietro Aric{\`o} and Gianluca Borghini and Gianluca Di Flumeri},
title = {A Novel Mutual Information based Feature Set for Drivers’ Mental Workload Evaluation using Machine Learning},
volume = {1},
pages = {1--23},
month = {August},
year = {2020},
journal = {Brain Sciences (Special Issue: Brain Plasticity, Cognitive Training and Mental States Assessment)},
url = {http://www.es.mdh.se/publications/5877-}
}