on005540
EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding
BEH EEG
EmoEEG-MC is a multi-context emotional EEG dataset comprising 64-channel EEG and peripheral physiological recordings from 60 participants exposed to video-induced and imagery-induced emotional stimuli across seven emotion categories (joy, inspiration, tenderness, fear, disgust, sadness, and neutral). This dataset addresses the critical gap in cross-context emotion decoding by enabling investigation of how emotional neural responses generalize across different elicitation contexts, with demonstrated classification accuracies of 66.7% for binary emotion classification and 28.9% for seven-category emotion classification using machine learning approaches.
- Participants
- 60
- Channels
- 64
- Size
- 70.4 GB
- Version
- vv1.0.0
- Updated
- Jul 10, 2026