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Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang, Shuo; Yin, Zhong; Wang, Yagang; Zhang, Wei; Wang, Yongxiong; Zhang, Jianhua
Journal article, Peer reviewed
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URI
https://hdl.handle.net/10642/8300
Date
2019-04-26
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Original version
Yang S, Yin Z, Wang, Zhang W, Wang, Zhang J. Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders. Computers in Biology and Medicine. 2019;109:159-170   https://dx.doi.org/10.1016/j.compbiomed.2019.04.034
Abstract
To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subject-specific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.
Publisher
Elsevier
Series
Computers in Biology and Medicine;Volume 109, June 2019
Journal
Computers in Biology and Medicine

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