Multiscale Temporal-Spectral-Spatial EEG Learning With Modality-Specific and Modality-Invariant Representations for Emotion Recognition
Multimodal physiological signals provide complementary cues for emotion recognition.
ProofPaper ↗
Key points
- Emotion-related EEG patterns vary across time, frequency bands, electrodes, and brain regions.
- This paper presents GMSI-Net, an EEG-centered multimodal emotion recognition framework.
- The temporal-spatial-spectral-region (TSSR) encoder models multiscale temporal-channel patterns, differential entropy features, and local-to-global brain-region relations.
- The modality-invariant representation (MIR) branch learns a shared affective representation.
Sources (1)
- [1]Multiscale Temporal-Spectral-Spatial EEG Learning With Modality-Specific and Modality-Invariant Representations for Emotion RecognitionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 02:35 AM
Multimodal physiological signals provide complementary cues for emotion recognition.
Emotion-related EEG patterns vary across time, frequency bands, electrodes, and brain regions.
Extractive summary: sentences quoted from the sources.
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