ArticleScientific data2025
A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding.
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 5 papers.
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
5 citing papers in PubMed.
- Responsible Stimulus Selection in Neuromarketing: A Critical Narrative Review and Normative Framework for Ethical, Sustainable, and Replicable Consumer Research.Behavioral sciences (Basel, Switzerland) · 2026Review
- A multimodal dataset for emotional transition analysis in virtual reality.Scientific data · 2026Article
- Evaluating the Impact of Demographic Factors on Subject-Independent EEG-Based Emotion Recognition Approaches.Diagnostics (Basel, Switzerland) · 2026Article
- Neural signature of human-dog interactions: EEG correlates from comparisons with plant and replica dog in a within-subject, cross-over randomized trial.Frontiers in human neuroscience · 2026Article
- A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
EEG-based emotion decoding is essential for unveiling neural mechanisms of emotion and has applications in mental health and human-machine interaction. However, existing datasets for EEG-based emotion decoding are limited to a single context of emotion elicitation. The ability of emotion decoding methods to generalize across different contexts remains underexplored. To address this gap, we present the Multi-Context Emotional EEG (EmoEEG-MC) dataset, featuring 64-channel EEG and peripheral physiological data from 60 participants exposed to two distinct contexts: video-induced and imagery-induced emotions. These contexts evoke seven distinct emotional categories: joy, inspiration, tenderness, fear, disgust, sadness, and neutral emotion. The emotional experience of specific emotion categories was validated through subjective reports. To validate the potential of cross-context emotion decoding, we implemented a support vector machine with L1 regularization, achieving accuracies of 66.7% for binary classification (positive vs. negative emotions) and 28.9% for seven-category emotion classification, both significantly above chance levels. The EmoEEG-MC dataset serves as a foundational resource for understanding the neural substrates of emotion and enhancing the real-world applicability of affective computing.
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.