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

- - 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—— 完整概述请参阅README.md文件。—— 本数据集收录了基于深度学习开展心理负荷（Mental Workload, MWL）检测研究的采集数据，作为学术出版物的补充数据公开，同时可供致力于利用多模态生理数据分类心理负荷的科研人员复用。本数据集采集自荷兰恩斯赫德特温特大学行为、管理与社会科学实验室，采集时段为2020年6月至7月。此前已有研究尝试通过多种生物信号开展心理负荷检测，近年来深度学习为脑机接口（Brain-Computer Interface, BCI）领域带来了全新的检测方法与研究成果。但当前多数相关研究仅采用单模态数据进行心理负荷分类，而诸多不同的信号模态已被证实对该任务极具应用价值。本数据集的核心目标是为构建基于多模态生理数据分类心理负荷的深度神经网络提供测试基准。本数据集配套的代码可通过以下GitHub仓库获取：https://github.com/Tech4People-BMSLab/mwl-detection，或通过以下DOI检索下载：https://doi.org/10.5281/zenodo.4043058