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. 

The link to the publication of 'Unobtrusive measurement of cognitive load and physiological signals in uncontrolled environments' will be added once the data descriptor was accepted in the respective journal.