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Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-0730-4405
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
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2019 (English)In: The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

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 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.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Autoencoder, Convolutional Neural Networks, Electroencephalography, Feature Extraction, Mental Workload
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-45059OAI: oai:DiVA.org:mdh-45059DiVA, id: diva2:1344975
Conference
The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 14 Nov 2019, Rome, Italy
Projects
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the roadAvailable from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved

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Barua, ShaibalAhmed, Mobyen UddinBegum, Shahina

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