Cross-task consistency of electroencephalography-based mental workload indicators: comparisons between power spectral density and task-irrelevant auditory event …

Y Ke, T Jiang, S Liu, Y Cao, X Jiao, J Jiang… - Frontiers in …, 2021 - frontiersin.org
Mental workload (MWL) estimators based on ongoing electroencephalography (EEG) and
event-related potentials (ERPs) have shown great potentials to build adaptive aiding …

Mental workload classification based on ignored auditory probes and spatial covariance

S Tang, C Liu, Q Zhang, H Gu, X Li… - Journal of neural …, 2021 - iopscience.iop.org
Objective. Estimation of mental workload (MWL) levels by electroencephalography (EEG)-
based mental state monitoring systems has been widely explored. Using event-related …

Reliability of mental workload index assessed by eeg with different electrode configurations and signal pre-processing pipelines

A Mastropietro, I Pirovano, A Marciano, S Porcelli… - Sensors, 2023 - mdpi.com
Background and Objective: Mental workload (MWL) is a relevant construct involved in all
cognitively demanding activities, and its assessment is an important goal in many research …

ERP based measures of cognitive workload: A review

U Ghani, N Signal, IK Niazi, D Taylor - Neuroscience & Biobehavioral …, 2020 - Elsevier
This review appraises electroencephalograph (EEG) approaches to cognitive workload
evaluation, focussing on the measurement of event-related potentials (ERPs) in single task …

An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task

Y Ke, H Qi, F He, S Liu, X Zhao, P Zhou… - Frontiers in human …, 2014 - frontiersin.org
Mental workload (MW)-based adaptive system has been found to be an effective approach
to enhance the performance of human-machine interaction and to avoid human error …

Efficient workload classification based on ignored auditory probes: a proof of concept

RN Roy, S Bonnet, S Charbonnier… - Frontiers in human …, 2016 - frontiersin.org
Mental workload is a mental state that is currently one of the main research focuses in
neuroergonomics. It can notably be estimated using measurements in …

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 …

Neurophysiological feature-based detection of mental workload by ensemble support vector machines

Z Yin, J Zhang, R Wang - … in Cognitive Neurodynamics (V) Proceedings of …, 2016 - Springer
The assessment of human operator mental workload (MWL) is crucial to prevent potential
accidents in human–machine collaborative systems. Continuous and objective evaluation of …

Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework

Z Yin, M Zhao, W Zhang, Y Wang, Y Wang, J Zhang - Neurocomputing, 2019 - Elsevier
Evaluating operator mental workload (MW) in human-machine systems via
neurophysiological signals is crucial for preventing unpredicted operator performance …

Cross-session classification of mental workload levels using EEG and an adaptive deep learning model

Z Yin, J Zhang - Biomedical Signal Processing and Control, 2017 - Elsevier
Abstract Evaluation of operator Mental Workload (MW) levels via ongoing
electroencephalogram (EEG) is quite promising in Human-Machine (HM) collaborative task …