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Deep Learning for A...
Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification
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- Islam, Mir Riyanul, Doctoral Student, 1991- (författare)
- Mälardalens universitet,Akademin för innovation, design och teknik
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- Barua, Shaibal (författare)
- Mälardalens högskola, Inbyggda system
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- Ahmed, Mobyen Uddin, Dr, 1976- (författare)
- Mälardalens högskola, Inbyggda system
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- Begum, Shahina, 1977- (författare)
- Mälardalens högskola, Inbyggda system
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- Flumeri, Gianluca Di (författare)
- Cognitive States in Operative Environment, BrainSigns, Via Sesto Celere, 7/C Rome, Italy
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(creator_code:org_t)
- 2019-10-10
- 2019
- Engelska.
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Ingår i: Communications in Computer and Information Science, Volume 1107. - Cham : Springer International Publishing. - 9783030324223 ; , s. 121-135
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- Autoencoder
- Convolutional Neural Networks
- Electroencephalography
- Feature Extraction
- Mental Workload
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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