Cross-task mental workload recognition based on EEG tensor representation and transfer learning

K Guan, Z Zhang, T Liu, H Niu - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
The accurate evaluation of mental workload of operators in human machine systems is of
great significance in ensuring the safety of operators and the correct execution of tasks …

Mental workload classification method based on EEG independent component features

H Qu, Y Shan, Y Liu, L Pang, Z Fan, J Zhang… - Applied Sciences, 2020 - mdpi.com
Excessive mental workload will reduce work efficiency, but low mental workload will cause a
waste of human resources. It is very significant to study the mental workload status of …

Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment

P Zhang, X Wang, W Zhang… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Mental workload assessment is essential for maintaining human health and preventing
accidents. Most research on this issue is limited to a single task. However, cross-task …

Mental workload classification in n-back tasks based on single trial EEG

Z Dai, A Bezerianos, ASH Chen, Y Sun - 2017 - dr.ntu.edu.sg
Mental workload estimation has been under extensive investigation over the years, because
the capability of monitoring the cognitive workload enables the prevention of cognitive …

Task-independent mental workload classification based upon common multiband EEG cortical connectivity

GN Dimitrakopoulos, I Kakkos, Z Dai… - … on Neural Systems …, 2017 - ieeexplore.ieee.org
Efficient classification of mental workload, an important issue in neuroscience, is limited, so
far to single task, while cross-task classification remains a challenge. Furthermore, network …

Cross-task cognitive workload recognition based on EEG and domain adaptation

Y Zhou, Z Xu, Y Niu, P Wang, X Wen… - … on Neural Systems …, 2022 - ieeexplore.ieee.org
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 …

Spectral and temporal feature learning with two-stream neural networks for mental workload assessment

P Zhang, X Wang, J Chen, W You… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
People's mental workload profoundly affects their work efficiency and health. Mental
workload assessment can be used to effectively avoid serious accidents caused by …

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 …

Feature weight driven interactive mutual information modeling for heterogeneous bio-signal fusion to estimate mental workload

P Zhang, X Wang, J Chen, W You - Sensors, 2017 - mdpi.com
Many people suffer from high mental workload which may threaten human health and cause
serious accidents. Mental workload estimation is especially important for particular people …

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

MR Safari, R Shalbaf, S Bagherzadeh, A Shalbaf - Scientific Reports, 2024 - nature.com
Mental workload refers to the cognitive effort required to perform tasks, and it is an important
factor in various fields, including system design, clinical medicine, and industrial …