ArticleScientific data2025
An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Physiological responses to emotional video stimuli: ECG, EDA, and temperature data.Data in brief · 2026Article
- An Enhanced Valence-Arousal Multimodal Emotion Dataset for Emotion Recognition.Scientific data · 2025Article
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Authors and funding
8 authors.
Funding
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.