ArticleScientific data2024
A Multimodal Dataset for Mixed Emotion Recognition.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- MindTS-MMD: A Chinese Multimodal Dataset for Emotion Expression Analysis in Children with Tourette Syndrome.Scientific data · 2026Article
- Article
- MoE-based multimodal alignment for vocal metaverse classrooms.Scientific reports · 2026Article
- An emotion recognition dataset using millimeter wave radar and physiological reference signals.Scientific data · 2026Article
- Continuous Emotion Recognition Using EDA-Graphs: A Graph Signal Processing Approach for Affective Dimension Estimation.Applied sciences (Basel, Switzerland) · 2026Article
- A multimodal drowsiness dataset using video, biometric, and behavioral data.Scientific data · 2026Article
- MetaEmo: a meta-learning and culturally adaptive framework for dynamic emotional recognition in cross-cultural classrooms.Frontiers in psychology · 2026Article
- LLaMAC: low-cost biosignal sensor based large multimodal dataset for affective computing.Scientific data · 2025Article
- Stress and Emotion Open Access Data: A Review on Datasets, Modalities, Methods, Challenges, and Future Research Perspectives.Journal of healthcare informatics research · 2025Review
- Cross-modal gated feature enhancement for multimodal emotion recognition in conversations.Scientific reports · 2025Article
- An emotion recognition method based on frequency-domain features of PPG.Frontiers in physiology · 2025Article
- Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
11 authors.
Funding
Abstract
Mixed emotions have attracted increasing interest recently, but existing datasets rarely focus on mixed emotion recognition from multimodal signals, hindering the affective computing of mixed emotions. On this basis, we present a multimodal dataset with four kinds of signals recorded while watching mixed and non-mixed emotion videos. To ensure effective emotion induction, we first implemented a rule-based video filtering step to select the videos that could elicit stronger positive, negative, and mixed emotions. Then, an experiment with 80 participants was conducted, in which the data of EEG, GSR, PPG, and frontal face videos were recorded while they watched the selected video clips. We also recorded the subjective emotional rating on PANAS, VAD, and amusement-disgust dimensions. In total, the dataset consists of multimodal signal data and self-assessment data from 73 participants. We also present technical validations for emotion induction and mixed emotion classification from physiological signals and face videos. The average accuracy of the 3-class classification (i.e., positive, negative, and mixed) can reach 80.96% when using SVM and features from all modalities, which indicates the possibility of identifying mixed emotional states.
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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.