ArticleScientific data2026
A multimodal dataset for emotional transition analysis in virtual reality.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- A multimodal dataset for emotional transition analysis in virtual reality.Scientific data · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Emotion recognition from physiological signals typically treats emotions as discrete, static states rather than dynamic processes, limiting real-world affective computing applications. This dataset contains multimodal physiological recordings from 28 participants experiencing systematically designed emotional transitions in virtual reality. Participants viewed emotion-eliciting video stimuli across three emotional quadrants with transition periods between stimuli. Four physiological modalities were recorded: EEG (7 channels, 300 Hz), ECG (4 leads, 512 Hz), EMG (2 channels, 512 Hz), and GSR (3 channels, 10 Hz). The protocol employed a balanced incomplete block design across six emotional sequences. Statistical validation shows quadrant differentiation with 70% physiologically validated and 85% self-reported emotion induction success rates on average. Individual journey analysis indicates that participants traversed between 8.84% and 58.39% of the theoretical maximum cumulative distance on the valence-arousal plane, reflecting substantial individual differences in emotional responsivity. The dataset comprises 1.84 GB of original XDF recordings, 238 video-aligned physiological segments, and self-assessment ratings. This resource enables research in dynamic emotion recognition, and individual differences in responsivity during controlled emotional transitions.
Indexed as
Identifiers
What OpenQuestion holds
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.