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

arXiv: https://arxiv.org/abs/2507.14698 · HTML: https://mlsanity.com/paper/2507.14698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 19 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Competence-based Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1903.09848/) (2019)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition](https://mlsanity.com/paper/2104.02935/) (2021)
- [ICLabel: An automated electroencephalographic independent component classifier, dataset, and website](https://mlsanity.com/paper/1901.07915/) (2019)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [Hybrid Curriculum Learning for Emotion Recognition in Conversation](https://mlsanity.com/paper/2112.11718/) (2021)
- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks](https://mlsanity.com/paper/2407.08164/) (2024)
- [Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2401.00167/) (2023)
- [GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy](https://mlsanity.com/paper/2312.09708/) (2023)
- [LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition](https://mlsanity.com/paper/2410.09767/) (2024)
- [Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training](https://mlsanity.com/paper/2211.11460/) (2022)
- [Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning](https://mlsanity.com/paper/2301.09237/) (2023)
- [CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning](https://mlsanity.com/paper/2308.02038/) (2023)
- [Contrastive Learning of Shared Spatiotemporal EEG Representations Across Individuals for Naturalistic Neuroscience](https://mlsanity.com/paper/2402.14213/) (2024)
- [Pre-trained Molecular Language Models with Random Functional Group Masking](https://mlsanity.com/paper/2411.01401/) (2024)
- [SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning](https://mlsanity.com/paper/2503.10100/) (2025)

## Built on by (most cited first, 1 of 1)

- [Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning](https://mlsanity.com/paper/2605.22379/) (2026)

## Abstract (arXiv)

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.