HEART: Hybrid Embedding for Affective and Riemannian Trajectories

Emotion recognition from electroencephalography (EEG) signals is a pivotal component of affective computing with broad applications in human-computer interaction, mental health, and emotion-aware systems. However, the intrinsic complexity and non-stationarity of EEG data pose significant challenges for accurate and robust emotion decoding. In this paper, we propose HEART (Hybrid Embedding for Affective and Riemannian Trajectories), a novel framework that jointly leverages Riemannian geometry-based spatial features and concept drift-inspired temporal segmentation to model the evolving neural dynamics underlying emotional experiences. HEART extracts covariance matrices from EEG segments, projects them into tangent spaces on the manifold of symmetric positive definite matrices, and employs geodesic distance measures to capture temporal transitions driven by data-adaptive segmentation. Furthermore, it integrates continuous Valence-Arousal-Dominance (VAD) regression with categorical emotion classification via a meta-classifier to exploit complementary affective representations. Evaluated on the FACED, SEED-VII and SEED datasets, HEART demonstrates superior performance compared to state-of-the-art methods, showing robustness to channel reduction and subject variability. The proposed framework offers a compact, interpretable, and effective solution for real-time EEG-based emotion recognition, advancing the integration of neurophysiological insights and machine learning for affective computing.