MGMCL: Multi-Granularity Manifold Contrastive Learning With Neural ODEs for Cross-Subject EEG Emotion Recognition
Cross-subject electroencephalogram (EEG)-based emotion recognition remains challenging due to substantial inter-individual variability and discrete formulation that overlooks affective continuity. Existing methods operate in Euclidean space and focus on marginal distribution alignment, failing to preserve the semantic structure of emotions across subjects. This article proposes MGMCL, reconceptualizing emotion recognition as learning continuous representations on symmetric positive definite (SPD
Record details
Published: 9 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 11 August 2026
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ethics.ai (9 August 2026), “MGMCL: Multi-Granularity Manifold Contrastive Learning With Neural ODEs for Cross-Subject EEG Emotion Recognition,” evidence record 18329, https://ethics.ai/record/18329 (originally published by arXiv cs.LG).
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