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Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers’ Mental Workload Classification

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Human Mental Workload: Models and Applications (H-WORKLOAD 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1107))

Abstract

In the pursuit of reducing traffic accidents, drivers’ mental workload (MWL) has been considered as one of the vital aspects. To measure MWL in different driving situations Electroencephalography (EEG) of the drivers has been studied intensely. However, in the literature, mostly, manual analytic methods are applied to extract and select features from the EEG signals to quantify drivers’ MWL. Nevertheless, the amount of time and effort required to perform prevailing feature extraction techniques leverage the need for automated feature extraction techniques. This work investigates deep learning (DL) algorithm to extract and select features from the EEG signals during naturalistic driving situations. Here, to compare the DL based and traditional feature extraction techniques, a number of classifiers have been deployed. Results have shown that the highest value of area under the curve of the receiver operating characteristic (AUC-ROC) is 0.94, achieved using the features extracted by convolutional neural network autoencoder (CNN-AE) and support vector machine. Whereas, using the features extracted by the traditional method, the highest value of AUC-ROC is 0.78 with the multi-layer perceptron. Thus, the outcome of this study shows that the automatic feature extraction techniques based on CNN-AE can outperform the manual techniques in terms of classification accuracy.

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Acknowledgement

This article is based on work performed in the project BrainSafeDrive. The authors would like to acknowledge the Vetenskapsrådet (The Swedish Research Council) for supporting the BrainSafeDrive project. The authors are also very thankful to Prof. Fabio Babiloni of BrainSigns. They would also like to acknowledge the extraordinary support of Dr. Gianluca Borghini & Dr. Pietro Aricó in experimental design & data collection. Further, authors would like to acknowledge the project students, Casper Adlerteg, Dalibor Colic & Joel Öhrling for their contribution to test the concept.

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Authors and Affiliations

  1. School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden

    Mir Riyanul Islam, Shaibal Barua, Mobyen Uddin Ahmed & Shahina Begum

  2. BrainSigns s.r.l., Rome, Italy

    Gianluca Di Flumeri

  3. Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy

    Gianluca Di Flumeri

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  1. Mir Riyanul Islam
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  2. Shaibal Barua
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  3. Mobyen Uddin Ahmed
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  4. Shahina Begum
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  5. Gianluca Di Flumeri
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Corresponding author

Correspondence to Mir Riyanul Islam .

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Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Technological University Dublin, Dublin, Ireland

    Maria Chiara Leva

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Islam, M.R., Barua, S., Ahmed, M.U., Begum, S., Di Flumeri, G. (2019). Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers’ Mental Workload Classification. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2019. Communications in Computer and Information Science, vol 1107. Springer, Cham. https://doi.org/10.1007/978-3-030-32423-0_8

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  • DOI: https://doi.org/10.1007/978-3-030-32423-0_8

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  • Online ISBN: 978-3-030-32423-0

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