A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders
2019
Computers in Biology and Medicine
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
doi:10.1016/j.compbiomed.2019.04.034
pmid:31059900
fatcat:jmcwu4s5prebtgpblqqajda6ja