Cross-subject workload classification with a hierarchical Bayes model
Most of the current EEG-based workload classifiers are subject-specific; that is, a new
classifier is built and trained for each human subject. In this paper we introduce a cross …
classifier is built and trained for each human subject. In this paper we introduce a cross …
[HTML][HTML] Multisubject “learning” for mental workload classification using concurrent EEG, fNIRS, and physiological measures
Y Liu, H Ayaz, PA Shewokis - Frontiers in human neuroscience, 2017 - frontiersin.org
An accurate measure of mental workload level has diverse neuroergonomic applications
ranging from brain computer interfacing to improving the efficiency of human operators. In …
ranging from brain computer interfacing to improving the efficiency of human operators. In …
Inter-subject cognitive workload estimation based on a cascade ensemble of multilayer autoencoders
Z Zheng, Z Yin, Y Wang, J Zhang - Expert Systems with Applications, 2023 - Elsevier
Abstract Machine learning approaches can build a computational model to predict cognitive
workload levels by using electroencephalogram (EEG) feature inputs at the same time …
workload levels by using electroencephalogram (EEG) feature inputs at the same time …
Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression
Y Ke, H Qi, L Zhang, S Chen, X Jiao, P Zhou… - International Journal of …, 2015 - Elsevier
Electroencephalographic (EEG) has been believed to be a potential psychophysiological
measure of mental workload. There however remain a number of challenges in building a …
measure of mental workload. There however remain a number of challenges in building a …
EEG-based multiclass workload identification using feature fusion and selection
The effectiveness of workload identification is one of the critical aspects in a monitoring
instrument of mental state. In this field, the workload is usually recognized as binary classes …
instrument of mental state. In this field, the workload is usually recognized as binary classes …
[HTML][HTML] EEG-based workload estimation across affective contexts
Workload estimation from electroencephalographic signals (EEG) offers a highly sensitive
tool to adapt the human–computer interaction to the user state. To create systems that …
tool to adapt the human–computer interaction to the user state. To create systems that …
Adaptive training using an artificial neural network and EEG metrics for within-and cross-task workload classification
CL Baldwin, BN Penaranda - NeuroImage, 2012 - Elsevier
Adaptive training using neurophysiological measures requires efficient classification of
mental workload in real time as a learner encounters new and increasingly difficult levels of …
mental workload in real time as a learner encounters new and increasingly difficult levels of …
Using cross-task classification for classifying workload levels in complex learning tasks
C Walter, S Schmidt, W Rosenstiel… - 2013 Humaine …, 2013 - ieeexplore.ieee.org
According to Cognitive Load Theory the type and amount of workload (WL) during learning
is crucial for successful learning and should be held within an optimal range of learners' …
is crucial for successful learning and should be held within an optimal range of learners' …
Beyond subjective self-rating: EEG signal classification of cognitive workload
Cognitive workload is an important indicator of mental activity that has implications for
human-computer interaction, biomedical and task analysis applications. Previously …
human-computer interaction, biomedical and task analysis applications. Previously …
Cross-task cognitive workload recognition based on EEG and domain adaptation
Cognitive workload recognition is pivotal to maintain the operator's health and prevent
accidents in the human-robot interaction condition. So far, the focus of workload research is …
accidents in the human-robot interaction condition. So far, the focus of workload research is …