Spatial-Temporal Transformer with Curriculum Learning for EEG-Based Emotion Recognition

TL;DR

This paper introduces SST-CL, a novel EEG emotion recognition framework combining spatial-temporal transformers with curriculum learning to improve robustness and accuracy across emotional intensities.

Contribution

It presents a new architecture integrating spatial-temporal transformers with a curriculum learning strategy for EEG-based emotion recognition.

Findings

Achieves state-of-the-art results on three benchmark datasets.

Effectively models inter-channel relationships and multi-scale temporal dependencies.

Demonstrates the importance of curriculum learning in handling emotional intensity variations.

Abstract

EEG-based emotion recognition plays an important role in developing adaptive brain-computer communication systems, yet faces two fundamental challenges in practical implementations: (1) effective integration of non-stationary spatial-temporal neural patterns, (2) robust adaptation to dynamic emotional intensity variations in real-world scenarios. This paper proposes SST-CL, a novel framework integrating spatial-temporal transformers with curriculum learning. Our method introduces two core components: a spatial encoder that models inter-channel relationships and a temporal encoder that captures multi-scale dependencies through windowed attention mechanisms, enabling simultaneous extraction of spatial correlations and temporal dynamics from EEG signals. Complementing this architecture, an intensity-aware curriculum learning strategy progressively guides training from high-intensity to low-intensity emotional states through dynamic sample scheduling based on a dual difficulty assessment. Comprehensive experiments on three benchmark datasets demonstrate state-of-the-art performance across various emotional intensity levels, with ablation studies confirming the necessity of both architectural components and the curriculum learning mechanism.