UNIVERSE: UNobtrusIVE measuRement of mental workload and stress in uncontrolled environments

A Dataset on Unobtrusive Measurement of Cognitive Load and Physiological Signals (EEG, PPG, EDA) in Uncontrolled Environments The dataset (approximately 315 hours in total) consists of physiological signals from wearable electroencephalography (EEG), electrodermal activity (EDA), photoplethysmogram (PPG), acceleration, and temperature sensors. The recorded dataset is curated from 24 participants following an eight-hour cognitive load elicitation paradigm. The mentioned consumer-grade physiological signals are obtained from the Muse S EEG headband and Empatica E4 wristband. The data is balanced across controlled and uncontrolled environments and high vs. low mental workload levels. During the study, participants worked on mental arithmetic, Stroop, N-Back, and Sudoku tasks in the controlled environment (roughly half of the data) and realistic home-office tasks such as researching, programming, and writing emails in uncontrolled environments. Data labels were obtained using Likert scales, Affective Sliders, PANAS, and NASA-TLX questionnaires. The completely anonymized data set and its publicly available features open a vast potential to the research community working on mental workload detection using consumer-grade wearable sensors. Among others, the data is suitable for developing real-time cognitive load detection methods, research on signal processing techniques for challenging environments, developing artifact removal techniques from low-cost wearable devices' data, or developing personal mental workload assistants. Follow the dataset descriptor publication for more details: Anders, C., Moontaha, S., Real, S., Arnrich, B.: Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments. Scientific Data 11(1), 1000 (2024). https://doi.org/https://doi.org/10.1038/s41597-024-03738-7《非侵入式测量非受控环境下认知负荷与生理信号（EEG、PPG、EDA）数据集》 本数据集总时长约315小时，涵盖可穿戴脑电图（Electroencephalography, EEG）、皮肤电活动（Electrodermal Activity, EDA）、光电容积描记图（Photoplethysmogram, PPG）、加速度及温度传感器采集的生理信号。该经整理的数据集源自24名受试者，采集过程遵循8小时认知负荷诱发范式。上述消费级生理信号采集自Muse S脑电图头带与Empatica E4腕带设备。 数据集在受控与非受控环境、高与低心理负荷水平两个维度上均实现样本平衡。实验期间，受试者在受控环境（约占总数据量的一半）中完成心算、斯特鲁普（Stroop）、N-Back及数独任务，在非受控环境中则开展调研、编程、撰写邮件等真实居家办公类任务。数据标签通过李克特量表（Likert Scales）、情感滑块量表（Affective Sliders）、正性负性情绪量表（Positive and Negative Affect Schedule, PANAS）以及NASA任务负荷指数量表（NASA Task Load Index, NASA-TLX）获取。 该完全匿名化的数据集及其公开可用的特征，为基于消费级可穿戴传感器开展心理负荷检测研究的科研群体提供了广阔的应用潜力。具体而言，本数据集可用于开发实时认知负荷检测方法、面向复杂环境的信号处理技术研究、基于低成本可穿戴设备数据的伪影去除技术，以及个人心理负荷辅助工具的研发。 如需了解更多细节，请参阅本数据集的相关发表文献： Anders, C.、Moontaha, S.、Real, S.、Arnrich, B.：《非受控环境下认知负荷与生理信号的非侵入式测量》（Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments），*Scientific Data*，11(1), 1000 (2024). https://doi.org/10.1038/s41597-024-03738-7