EMO-CARE: EEG Multi-Scale Temporal Modeling With Channel-Aware Feature Attention for Robust Subject-Independent Emotion Recognition

Electroencephalography (EEG)-based emotion recognition holds promise for real-time mental health monitoring, adaptive interfaces, and affective computing. However, accurate prediction across individuals remains challenging due to inter-subject variability and the non-stationary nature of EEG signals. To address this, we propose EMO-CARE, a lightweight deep learning framework that integrates multi-scale temporal convolutional networks with feature-level self-attention operating on multi-scale temporal representations. This architecture captures emotional patterns across diverse neural timescales while adaptively weighting multi-scale temporal features based on their relevance. Evaluated under the rigorous Leave-One-Subject-Out (LOSO) protocol on three benchmark datasets: SEED, SEED-V, and DREAMER, EMO-CARE achieves state-of-the-art accuracy with low inference latency. Extensive ablation experiments demonstrate the contribution of each architectural component, and the learned attention patterns align with known emotion-related neural activity. These findings collectively highlight EMO CARE's effectiveness in achieving subject-independent generalization and real-time applicability for EEG based emotion recognition.