Perceived Mental Workload Detection using Multimodal Physiological Data - Deep Learning, GitHub Linked
doi:10.4121/12932801.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future.
For a link that will always point to the latest version, please use
doi: 10.4121/12932801
doi: 10.4121/12932801
Datacite citation style:
Tenzing Dolmans; Mannes Poel; Jan-Willem van 't Klooster; Bernard P. Veldkamp (2020): Perceived Mental Workload Detection using Multimodal Physiological Data - Deep Learning, GitHub Linked. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/12932801.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
CC0
- - See README.md for a more complete overview. - -
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.
Please refer to the following GitHub repository for the code that was used to create this dataset: https://github.com/Tech4People-BMSLab/mwl-detection, or find it using the following DOI: https://doi.org/10.5281/zenodo.4043058
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.
Please refer to the following GitHub repository for the code that was used to create this dataset: https://github.com/Tech4People-BMSLab/mwl-detection, or find it using the following DOI: https://doi.org/10.5281/zenodo.4043058
history
- 2020-09-22 first online, published, posted
publisher
4TU.ResearchData
format
.rar, .md, .csv, .tfrecord, .xdf
references
funding
- OP-OOST EFRO PROJ-00900
organizations
University of Twente, Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS); Faculty of Behavioural, Management and Social Sciences (BMS)
DATA
files (2)
- 4,126 bytesMD5:
5d7e824aaa74390fedc3f0ddd2532e2bREADME.md - 1,498,949,480 bytesMD5:
faa85756a57526e81ebb10255660382dData.rar -
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1,498,953,606 bytes unzipped
