Emotion recognition based on electroencephalography (EEG) signals has emerged as a prominent research field, facilitating objective evaluation of diseases like depression and motion detection for heathy people. Starting from the basic concepts of temporal-frequency-spatial features in EEG and the methods for cross-domain feature fusion. This survey then extends the overfitting challenge of EEG single-modal to the problem of heterogeneous modality modeling in multi-modal conditions. It explores issues such as feature selection, sample scarcity, cross-subject emotional transfer, physiological knowledge discovery, multi-modal fusion methods and modality missing. These findings provide clues for researchers to further investigate emotion recognition based on EEG signals.

基于脑电图（EEG）信号的情绪识别已成为一个突出的研究领域，有助于客观评估抑郁症等疾病和健康人的运动检测。从脑电中时频空特征的基本概念和跨域特征融合的方法出发。然后，这项调查将脑电图单模态的过度拟合挑战扩展到多模态条件下的异质模态建模问题。它探讨了特征选择、样本稀缺、跨学科情感转移、生理知识发现、多模态融合方法和模态缺失等问题。这些发现为研究人员进一步研究基于脑电图信号的情绪识别提供了线索。