Peripheral physiological signals and subjectively felt intensity during an emotion recognition experiment

The dataset contains peripheral physiological signals recorded during an emotion inducing experiment, in which subjects could express their subjectively feelings in real-time. In the experiment, there are intensity-varying trials for three different qualities. For each subject, the dataset includes:

Metadata: gender and age;

Physiological signals: raw galvanic skin response, pulse and respiration signals;

Subjective feeling: real-time subjectively felt emotion intensity;

Context information: stimuli indexes.

This dataset’s primary goal is to support the development of emotion recognition algorithms that account for emotion dynamics by overcoming limitations of typical emotion recognition datasets in which, over an extended period of time corresponding to a task, subjects provide their felt emotional state with a single label / dimensions for the entire interval. Such static labelling does not easily allow for the depicting of the dynamic nature of emotions, since they do not contain information about feelings throughout the interval. On the other hand, incorporating emotion dynamics in emotion recognition holds the potential for better recognition and interpretation capabilities.

Beyond experts in emotion recognition, this dataset is also valuable to experts in psychological, cognitive neuroscience, and related fields, since it enables exploring relationships between physiological signals and emotional patterns.

本数据集包含情绪诱发实验期间记录的外周生理信号，实验中受试者可实时表达其主观感受。实验中设有针对三种不同特质的强度可变试次。针对每位受试者，数据集包含以下内容：
元数据：性别与年龄；
生理信号：原始皮肤电反应（galvanic skin response）、脉搏信号与呼吸信号；
主观感受：实时记录的主观感知情绪强度；
上下文信息：刺激物索引。

本数据集的核心目标是助力情绪识别算法的开发，使其能够刻画情绪动态性——现有典型情绪识别数据集存在局限：在对应任务的较长时段内，受试者仅能为整个区间提供单一标签/维度的情绪状态自评，此类静态标注难以呈现情绪的动态本质，因其未包含时段内的情绪感受变化信息。而将情绪动态性融入情绪识别研究，则有望实现更优异的识别与解读性能。

除情绪识别领域的研究者外，本数据集对心理学、认知神经科学及相关领域的专家也具有重要价值，因其可用于探索生理信号与情绪模式之间的关联。