Exploring emotional experiences and dataset construction in the era of short videos based on physiological signals

作者：Yilong Liao, Yuan Gao, Fang Wang, Zhongwen Xu, Yifan Wu, Li Zhang · 发表于：Biomedical Signal Processing and Control · 年份：2024 · DOI：10.1016/j.bspc.2024.106648 · 被引用次数：15 · 研究领域：Emotion and Mood Recognition、EEG and Brain-Computer Interfaces、Mental Health Research Topics

This study aims to uncover the intrinsic links between emotional experiences and physiological responses as users watch short videos on social media platforms, with a particular focus on using changes in physiological signals to identify and understand different emotional states. To achieve this objective, a simulated experiment was designed to browse short videos and induce and record physiological signals under seven typical emotional states. The recorded signals included electroencephalogram, galvanic skin response, skin temperature, and heart rate, resulting in the creation of a dataset. Machine learning algorithms were employed to classify emotions and evaluate the dataset’s effectiveness. Statistical testing methods were used to analyze signal feature changes and distributions across different emotional states, exploring trends and their statistical significance. The study successfully constructed an emotion-physiological signal dataset. Statistical tests revealed significant changes in physiological signal characteristics across different emotional states, providing extensive data support for understanding how emotions specifically affect physiological responses. The research not only confirmed the practicality of the constructed dataset in emotion recognition tasks but also provided empirical evidence of how emotions influence physiological responses through detailed analysis of physiological signals. The findings of this study hold significant value for emotional sci...