从片段到轨迹：基于证据检索的演化情感图用于连续脑电情绪识别

中文摘要 AI 辅助

基于脑电图（EEG）的情绪识别对于情感计算和人机交互具有重要意义，然而现有的大多数方法将一次较长的试验划分为多个短片段，并为每个片段分配其来源试验的标签。尽管这一策略增加了训练样本的数量，但它将演化的情绪反应简化为一个片段级、粗粒度且静态的预测问题。在现实中，随着刺激的展开，情绪可能持续涌现、增强、减弱并波动，这促使研究者从完整的EEG试验中预测时间对齐的情感轨迹。这一任务要求协同建模空间神经组织在整个试验过程中的演化方式，以及局部情绪波动与长期趋势之间的相互作用。在本工作中，我们正式定义并系统研究了将连续EEG情绪识别视为全试验情感轨迹预测的问题。我们提出了EAGER，一种带有证据检索的演化情感图框架，用于连续EEG情绪识别。EAGER包含两个互补模块：情感状态引导的拓扑演化模块对EEG活动的演化空间组织进行建模，而多尺度时间证据检索模块将短期波动与更长时间范围的时间趋势相结合，以实现时间对齐的预测。在MAHNOB-HCI、SEED-VII和REFED上的实验表明，与代表性方法相比，EAGER在轨迹跟踪指标上取得了一致的提升，同时具有有竞争力的逐点误差。

英文摘要

Electroencephalography (EEG)-based emotion recognition is important for affective computing and human-computer interaction, yet most existing methods divide a long trial into short segments and assign each segment the label of its source trial. Although this strategy increases the number of training samples, it reduces an evolving emotional response to a segment-level, coarse-grained, and static prediction problem. In reality, emotion may continuously emerge, intensify, weaken, and fluctuate as a stimulus unfolds, motivating the prediction of a time-aligned affective trajectory from the complete EEG trial. This task requires coordinated modeling of how spatial neural organization evolves throughout the trial and how local emotional fluctuations interact with longer-term trends. In this work, we formally define and systematically investigate continuous EEG emotion recognition as whole-trial affective trajectory prediction. We propose EAGER, an Evolving Affective Graph framework with Evidence Retrieval for continuous EEG emotion recognition. EAGER comprises two complementary modules: Affective State-guided Topology Evolution models the evolving spatial organization of EEG activity, while Multi-scale Temporal Evidence Retrieval integrates short-term fluctuations with longer-range temporal trends for time-aligned prediction. Experiments on MAHNOB-HCI, SEED-VII, and REFED show consistent gains in trajectory-tracking metrics over representative methods, with competitive pointwise errors.

发表机构

• College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）
• School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）
• School of Intelligent Science and Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）智能科学与工程学院）
• School of Public Health, National University of Singapore（新加坡国立大学公共卫生学院）
• Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）

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