Perceived Mental Workload Detection using Multimodal Physiological Data - Deep Learning, GitHub Linked



This dataset contains data collected during research into mental workload (MWL) detection using deep learning. It is being made public as supplementary data for publications, as well as for reuse in research that seeks to classify MWL using multimodal physiological data.The data in this dataset was collected in the Behavioural, Management, and Social Sciences Lab, University of Twente, Enschede, The Netherlands in June/July 2020. Mental workload detection has been attempted using various bio-signals. Recently, deep learning has allowed for novel methods and results within the BCI community. However, studies currently often only use a single modality to classify mental workload, whereas a plethora of modalities have proven to be valuable in this task. The goal of this dataset is to serve as a testing ground for the creation of deep neural networks that can classify MWL using multimodal physiological data.
Date made available22 Sept 2020
Publisher4TU.Centre for Research Data

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