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

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 pr...

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Veröffentlicht in:	Computers in biology and medicine Jg. 109; S. 159 - 170
Hauptverfasser:	Yang, Shuo, Yin, Zhong, Wang, Yagang, Zhang, Wei, Wang, Yongxiong, Zhang, Jianhua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.06.2019 Elsevier Limited
Schlagworte:	Accuracy Algorithms Artificial intelligence Classification Classifiers Cognitive ability Deep learning EEG Electroencephalogram Electroencephalography Human performance Human-machine system Internal Medicine Mapping Mental workload Neural networks Noise reduction Nuclear power plants Operator performance Other Physiology Preservation Stacked denoising autoencoder Task complexity Workload Workloads Deep learning Electroencephalogram Mental workload Human-machine system Stacked denoising autoencoder
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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