重性抑郁障碍（Major Depressive Disorder，MDD）作为一种全球高发且致残率高的心理健康疾病，其 诊断主要依赖临床问诊与量表评估，目前仍缺乏可靠的客观生物标志物。事件相关电位（Event-related Potential，ERP）作为一种无创、实时且具有毫秒级时间分辨率的脑功能检测技术，能直接反映在认知、情感任 务时的神经活动，为MDD的诊断和治疗提供了新的视角。本综述系统总结ERP在MDD认知与情感加工 异常研究中的关键发现，表明多个脑电成分在MDD患者中呈现特征性改变，反映其认知控制、情绪调节及 奖赏处理等多阶段功能障碍。尽管这些指标普遍存在跨诊断异质性和状态依赖性，但其动态变化模式及多 成分联合分析在辅助诊断、疗效预测和包括药物治疗、神经调控及心理干预的治疗监测中展现出应用潜 力。当前研究仍面临范式标准化不足、临床异质性及可重复性等挑战。未来应通过多中心协作、多模态数 据融合与机器学习建模，推动ERP从实验指标向临床实用工具的转化，最终服务于MDD的个体化诊疗。

Major Depressive Disorder (MDD) is a globally prevalent mental health condition with high disability rates. Its diagnosis currently relies primarily on clinical interviews and scale assessments, and there remains a lack of reliable objective biomarkers. Event-related potential (ERP), as a non-invasive, real-time brain function detection technology with millisecond-level temporal resolution, can directly reflect neural activities during cognitive and emotional tasks, providing a new perspective for the diagnosis and treatment of MDD. This review systematically summarizes key findings from ERP studies on cognitive and emotional processing abnormalities in MDD, indicating that multiple ERP components exhibit characteristic alterations in MDD patients, reflecting dysfunctions in cognitive control, emotion regulation, and reward processing across multiple stages. Although these indicators commonly demonstrate transdiagnostic heterogeneity and state-dependent variability, their dynamic patterns and multi-component integrated analyses show potential for assisting in diagnosis, predicting treatment efficacy, and monitoring interventions including pharmacotherapy, neuromodulation, and psychological therapy. Current research still faces challenges such as insufficient paradigm standardization, clinical heterogeneity, and reproducibility issues. Future efforts should focus on promoting multi-center collaboration, integrating multimodal data, and employing machine learning modeling to advance the translation of ERPs from experimental markers to clinically practical tools, ultimately contributing to personalized diagnosis and treatment of MDD.