An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition.

ABSTRACT: We introduce a novel multimodal emotion recognition dataset designed to enhance the precision of valence-arousal modeling while incorporating individual differences. This dataset includes electroencephalogram (EEG), electrocardiogram (ECG), and pulse interval (PI) data from 64 participants. Data collection employed two emotion induction paradigms: video stimuli targeting different valence levels (positive, neutral, and negative) and the Mannheim Multicomponent Stress Test (MMST) inducing high arousal through cognitive, emotional, and social stressors. To enrich the dataset, participants' personality traits, anxiety, depression, and emotional states were assessed using validated questionnaires. By capturing a broad spectrum of affective responses and systematically accounting for individual differences, this dataset provides a robust resource for precise emotion modeling. The integration of multimodal physiological data with psychological assessments lays a strong foundation for personalized emotion recognition. We anticipate this resource will support the development of more accurate, adaptive, and individualized emotion recognition systems across diverse applications.

SUBMITTER: Huang X 

PROVIDER: S-EPMC12705992 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature