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Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-0730-4405
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-7305-7169
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0003-3802-4721
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0002-1212-7637
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2019 (engelsk)Inngår i: Communications in Computer and Information Science, Volume 1107, 2019, s. 121-135Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2019. s. 121-135
Emneord [en]
Autoencoder, Convolutional Neural Networks, Electroencephalography, Feature Extraction, Mental Workload
HSV kategori
Identifikatorer
URN: urn:nbn:se:mdh:diva-45059DOI: 10.1007/978-3-030-32423-0_8Scopus ID: 2-s2.0-85075680380ISBN: 9783030324223 (tryckt)OAI: oai:DiVA.org:mdh-45059DiVA, id: diva2:1344975
Konferanse
The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 14 Nov 2019, Rome, Italy
Prosjekter
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the roadTilgjengelig fra: 2019-08-22 Laget: 2019-08-22 Sist oppdatert: 2019-12-16bibliografisk kontrollert

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Islam, Mir RiyanulBarua, ShaibalAhmed, Mobyen UddinBegum, Shahina

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