Prolonged stress and high mental workload can have deteriorating long-term effects developing several stress-related diseases. The existing stress detection techniques are often uni-modal and limited to controlled setups. One sensing modality could be unobtrusive but mostly results in unreliable sensor readings, especially in uncontrolled environments. Our study recorded multi-modal physiological signals from twenty-five participants in controlled and uncontrolled environments by performing given and self-chosen tasks of high and low mental demand. In this version, we processed and published a subset of the dataset from six participants while working on the rest. The subset of the data is used to check the feasibility of our study by engineering features from electroencephalography (EEG), photoplethysmography (PPG), electrodermal activity (EDA), and temperature sensor data. Machine learning methods were used for the binary classification of the tasks. Personalized models in the uncontrolled environment achieved a mean classification accuracy of up to 83% while using one of the four labels, unveiling some unintentional mislabeling by participants. In controlled environments, multi-modality improved the accuracy by at least 7%. Generalized machine learning models achieved close to chance-level performances. This work underlines the importance of multi-modal recordings and provides the research community with an experimental paradigm to take studies of mental workload and stress workload and stress out of controlled into uncontrolled environments

In the current version, we are providing the engineered features extracted from the raw data. In version 2, we will provide the raw data of approximately 20 participants. Update on September 2024: We no longer give access to this dataset since we have published a more extensive data set lately in version 2, which will be more beneficial for the research community. Please check our publication at https://www.nature.com/articles/s41597-024-03738-7. You will find relevant information on the new data source here.