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- W2938850436 abstract "Abstract According to the literature, mental workload (MW) is defined as the interaction of mental demands imposed on operators by tasks they attend to and the effort they put into achieving these tasks. An interesting prospect to assess MW is to consider the use of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which allow nonintrusive probing of a user's state. Several features can be extracted from the signal collected by their respective sensors, such as the power spectral density in different frequency bands for the EEG, and the level of oxygenated hemoglobin (HbO) for the fNIRS. These features are classically found to vary depending on the MW imposed on the participants. Additional features can be derived from the EEG and fNIRS signals such as connectivity features. Recently, Charbonnier and collaborators showed that connectivity features computed between EEG electrodes and the Frobenius distance between the matrices of connectivity features could be used to estimate quite accurately their mental state." @default.
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- W2938850436 date "2018-01-01" @default.
- W2938850436 modified "2023-10-03" @default.
- W2938850436 title "EEG and FNIRS Connectivity Features for Mental Workload Assessment" @default.
- W2938850436 cites W2285375918 @default.
- W2938850436 doi "https://doi.org/10.1016/b978-0-12-811926-6.00098-1" @default.
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