Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals. Unlike conventional discrete emotion recognition, continuous prediction requires capturing long-range temporal dependencies and coherent emotional dynamics. However, existing methods mainly rely on point-wise regression and directly model noisy high-dimensional EEG features, limiting their ability to characterize continuous emotional evolution.To address these challenges, we propose EEGDancer, a dynamic emotional latent space learning framework for continuous EEG emotion prediction. The framework integrates vector-quantized representation learning, masked temporal modeling, and reinforcement learning-based trajectory optimization into a unified architecture.Specifically, a causal spatiotemporal Vector-Quantization Variational Autoencoder (VQ-VAE) is designed to learn structured emotional prototypes and construct a discrete-continuous emotional latent space from EEG signals. Based on the learned latent representations, a Transformer-based masked dynamic modeling strategy captures long-range emotional dependencies and temporal evolution patterns. Furthermore, continuous emotion prediction is formulated as a sequential decision-making problem, and a Soft Actor-Critic (SAC) framework is introduced to optimize emotional prediction trajectories at the sequence level instead of frame-wise local fitting.Extensive experiments on the SEED, SEED-IV, and Long-Term Naturalistic Emotion datasets demonstrate that EEGDancer consistently outperforms existing machine learning and deep learning methods. Ablation studies further verify the effectiveness of the proposed latent space and reinforcement learning-based trajectory optimization for modeling continuous EEG emotional dynamics.

翻译：连续脑电情绪预测旨在从脑电信号中建模人类情感状态随时间演化的过程。不同于传统离散情绪识别，连续预测需要捕捉长程时间依赖与连贯的情感动态。然而，现有方法主要依赖逐点回归并直接建模噪声高维脑电特征，限制了其对连续情感演化的表征能力。为应对这些挑战，我们提出EEGDancer——一种面向连续脑电情绪预测的动态情感隐空间学习框架。该框架将向量量化表征学习、掩码时序建模与基于强化学习的轨迹优化整合为统一架构。具体而言，我们设计了因果时空向量量化变分自编码器（VQ-VAE），学习结构化情感原型并从脑电信号构建离散-连续情感隐空间。基于学习到的隐表征，采用Transformer掩码动态建模策略捕获长程情感依赖与时序演化模式。此外，我们将连续情绪预测建模为序列决策问题，引入柔性演员-评论家（SAC）框架优化序列级情感预测轨迹，而非逐帧局部拟合。在SEED、SEED-IV及长期自然情感数据集上的大量实验表明，EEGDancer持续优于现有机器学习和深度学习方法。消融实验进一步验证了所提出的隐空间与基于强化学习的轨迹优化对建模连续脑电情感动态的有效性。