… used to estimate workload using Electroencephalography (EEG) data. An open-access EEG
… In this paper, the goal is to classify mental workload classification on EEG data using MLP. …

… The study reported in this paper explored the use of EEG as a tool to track individual human
capabilities of the subjects in real-time (as individually perceived mental workload metric) …

… mental workload assessment, nor evaluated the stability in time of these markers and of the
performance of the associated mental workload … workload estimation can be achieved using …

… have also been explored to achieve workload classification in [12]. … EEG has sensitivity to
change with mental workload. Thus, these methods have potential for being used as workload …

… In this work, we propose an approach to estimate the mental workload using
electroencephalogram (EEG) signals of an operator while in operation. A thorough investigation of …

… In this section, we discuss mental workload estimation using the proposed deep learning
approach. The section consists of four subsections, namely data acquisition & preprocessing (…

… In [72] we propose a three-dimensional convolutional neural network (3D CNN) employing
a multilevel feature fusion algorithm for mental workload estimation using EEG signals. The …

… However, it is extremely difficult to assess the mental/cognitive demand of construction tasks
… a novel electroencephalography (EEG) approach to estimate task mental workload based …

… of mental workload is Electroencephalogram (EEG), which includes directly recording the
activity of the brain and is more complex and expensive to employ. EEG, … of mental demands …

… The mental workload level was assessed by using the deep learning classifier and EEG …
The brief literature review for MW recognition using EEG power features and machine-learning-…