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Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification
Open AccessArticle

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

1
School of Innovation, Design and Engineering, Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden
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BrainSigns srl, Lungotevere Michelangelo 9, 00192 Rome, Italy
3
Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(8), 551; https://doi.org/10.3390/brainsci10080551
Received: 15 June 2020 / Revised: 3 August 2020 / Accepted: 11 August 2020 / Published: 13 August 2020
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a 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 the 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. View Full-Text
Keywords: electroencephalography; feature extraction; machine learning; mental workload; mutual information; vehicular signal electroencephalography; feature extraction; machine learning; mental workload; mutual information; vehicular signal
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Islam, M.R.; Barua, S.; Ahmed, M.U.; Begum, S.; Aricò, P.; Borghini, G.; Di Flumeri, G. A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning. Brain Sci. 2020, 10, 551.

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