on005540
EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding
BEH EEG Zarr
EmoEEG-MC is a multi-context emotional EEG dataset containing 64-channel EEG and peripheral physiological signals (PPG and GSR) from 60 participants. Emotions were induced in two contexts, video viewing and mental imagery, and covered seven categories: joy, inspiration, tenderness, fear, disgust, sadness and neutral. The dataset is intended to support cross-context emotion decoding, and SVM baselines with L1 regularization achieved above-chance accuracy for binary and seven-category classification.
- Participants
- 60
- Channels
- 64
- Size
- 70.4 GB
- Version
- v1.0.0
- Updated
- Jun 27, 2026