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.

CLAS（认知负荷、情感与压力）数据集旨在构建一个开放获取的资料库，其专门开发目的在于支持对特定心理状态及个体情感状况的自动化评估研究。该资源旨在助力RTD活动，旨在开发融合自动化识别人类情感、自动检测压力状况、自动评估专注程度、认知负荷及瞬时认知能力等功能的人工智能人机交互（HCI）界面。此外，该数据集还可解释与在严格时间限制下快速解决逻辑和数学问题相关的一些人格特质。数据集包含62名健康志愿者参与三项交互任务和两项感知任务时同步记录的生理信号，如心电图（ECG）、体积描记图（PPG）、皮肤电活动（EDA）以及加速度计数据，以及元数据。交互任务旨在激发不同类型的认知努力，包括解决数学问题序列、逻辑问题以及斯特鲁普测试。感知任务利用图像和视听刺激，这些刺激被特意挑选以激发唤醒-效价空间四个象限中的情感。