… In this study, the integration of EEG, fNIRS, and physiological signals was investigated for
the classification of three workload levels induced by the n-back working memory task. The …

… We studied the capability of a Hybrid functional neuroimaging technique to quantify human
mental workload (MWL). We have used electroencephalography (EEG) and functional near-…

… EEG and fNIRS. Therefore, both the existing CNN architectures and fNIRS-EEG input must
be adapted to allow fNIRS-EEG … of CNN classification of mental workload tasks. This study …

… EEG+fNIRS is still a relatively new and underexplored … state of the art whole-head EEG-fNIRS
set up and show that it is … and extracted EEG, fNIRS, and hybrid (both EEG and fNIRS) …

When interacting with technical systems, users experience mental workload. Particularly in …
' workload and dynamically adapt the behavior of the interface to the measured workload. …

… fNIRs provides good spatial localization relative to EEG, on the order of 1cm2, and has the
capacity to be integrated with EEG… the use of fNIRs as a measure of workload. Current models …

… The main objective of this study is to investigate the fusion of EEG and fNIRS to discriminate
mental workload levels. We adopted the n-back working memory task, which has been used …

… -channel electroencephalography (EEG) data… EEG–functional near-infrared spectroscopy
(EEG–fNIRS), supported by machine-learning features to deal with multi-level mental workload …

… mental workload cognitive dimension was added to the conventional Face-based P300 speller
to investigate hybrid EEG-fNIRS … based Face paradigm for fNIRS modality for comparison …

… In conclusion, this study was to construct a more accurate and convenient EEG–fNIRS-based
mental workload detection method by optimizing the signal acquisition configuration. The …