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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Computers in biology and medicine Ročník 109; s. 159 - 170
Hlavní autoři:	Yang, Shuo, Yin, Zhong, Wang, Yagang, Zhang, Wei, Wang, Yongxiong, Zhang, Jianhua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.06.2019 Elsevier Limited
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!