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A multimodal dataset for emotional transition analysis in virtual reality
Namazbai Ishmakhametov1, Mohammad Y M Naser2, Selam B Kelil2
1Department of Data Science and Analytics, Kennesaw State University, Marietta, GA, 30060, USA.
Scientific Data
|May 20, 2026
Summary
This study introduces a new dataset for dynamic emotion recognition, capturing physiological signals during virtual reality emotional transitions. It highlights significant individual differences in emotional responses, paving the way for more realistic affective computing.
Area of Science:
- Affective computing and computational neuroscience.
- Multimodal physiological signal processing.
Background:
- Current emotion recognition models often simplify emotions as static states, limiting their real-world applicability.
- Dynamic emotional transitions and individual differences are crucial for advancing affective computing.
Purpose of the Study:
- To introduce a novel multimodal physiological dataset designed for studying dynamic emotion recognition.
- To investigate individual differences in emotional responsivity during controlled emotional transitions.
Main Methods:
- Collected multimodal physiological data (EEG, ECG, EMG, GSR) from 28 participants experiencing emotional transitions in virtual reality.
- Utilized a balanced incomplete block design with emotion-eliciting video stimuli across valence-arousal quadrants.
- Performed statistical validation for emotion induction success and analyzed individual emotional trajectories.
Main Results:
- Achieved 70% physiological and 85% self-reported emotion induction success rates, validating quadrant differentiation.
- Demonstrated substantial individual variability in emotional responses, with participants covering significant portions of the valence-arousal plane.
- The dataset includes raw recordings, processed segments, and self-assessment ratings.
Conclusions:
- The dataset provides a valuable resource for researching dynamic emotion recognition and individual differences in emotional processing.
- This work advances the development of more nuanced and realistic affective computing systems.
- Future research can leverage this dataset to explore personalized emotion models and adaptive interfaces.
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