Database for Cognitive Load Affect and Stress recognition
The CLAS (Cognitive Load, Affect and Stress) dataset was conceived as a freelyaccessible repository which is purposely developed to support research on the automated assessment of certain states of mind and the emotional condition of a person. This resource is intended to support RTD activities aiming at the development of intelligent human computer interaction (HCI) interfaces that incorporate functionalities allowing for the automated recognition of human emotions, the automated detection of stress conditions, the automated assessment of the degree of concentration, cognitive load, and momentary cognitive capacity, and can account for some personality traits related to the ability to quickly solve logical and mathematical problems under strict time constraints.
The dataset consists of synchronized recordings of physiological signals, such as Electrocardiography (ECG), Plethysmography (PPG), ElectroDermal Activity (EDA), as well as accelerometer data, and metadata of 62 healthy volunteers, which were recorded while involved in three interactive tasks and two perceptive tasks. The interactive tasks aim to elicit different types of cognitive effort and included solving sequences of Math problems, Logic problems and the Stroop test. The perceptive tasks make use of images and audio-video stimuli, purposely selected to evoke emotions in the four quadrants of the arousalvalence space.
1. Database structure (CLAS.zip)
The database is organized in 4 folders:
· Answers – answers of the questions in the interactive tasks (Math problems, Logic problems and the Stroop test) for each person.
· Block_details – metadata for each block (1 block per task) for every participant.
· Data – raw signal recordings for the individual participants.
· Documentation – accompanying documents.
When using the CLAS dataset, please cite:
Markova, V., Ganchev, T., Kalinkov, K. (2019). CLAS: A Database for Cognitive Load, Affect and Stress Recognition, in Proceedings of the International Conference on Biomedical Innovations and Applications, (BIA-2019), art. no. 8967457, DOI: 10.1109/BIA48344.2019.8967457. Available on-line: https://ieeexplore.ieee.org/document/8967457
- CLAS_Database.zip (3.31 GB)
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