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EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding
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.
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Coming soon. Per-file data-quality summaries are precomputed by the NEMAR processing pipeline. The static aggregate is on the way — tracked at nemar-cli#511.