Electroencephalography (EEG) underpins neuroscience, clinical neurophysiology, and brain-computer interfaces (BCIs), yet pronounced inter- and intra-subject variability limits reliability, reproducibility, and translation. This systematic review studies that quantified or modeled EEG variability across resting-state, event-related potentials (ERPs), and task-related/BCI paradigms (including motor imagery and SSVEP) in healthy and clinical cohorts. Across paradigms, inter-subject differences are typically larger than within-subject fluctuations, but both affect inference and model generalization. Stability is feature-dependent: alpha-band measures and individual alpha peak frequency are often relatively reliable, whereas higher-frequency and many connectivity-derived metrics show more heterogeneous reliability; ERP reliability varies by component, with P300 measures frequently showing moderate-to-good stability. We summarize major sources of variability (biological, state-related, technical, and analytical), review common quantification and modeling approaches (e.g., ICC, CV, SNR, generalizability theory, and multivariate/learning-based methods), and provide recommendations for study design, reporting, and harmonization. Overall, EEG variability should be treated as both a practical constraint to manage and a meaningful signal to leverage for precision neuroscience and robust neurotechnology.

翻译：脑电图（EEG）是神经科学、临床神经生理学和脑机接口（BCI）研究的基础，然而显著的个体间与个体内变异性限制了其可靠性、可重复性及实际转化应用。本系统性综述研究了在健康人群和临床队列中，针对静息态、事件相关电位（ERPs）以及任务相关/BCI范式（包括运动想象和稳态视觉诱发电位）中EEG变异性的量化或建模工作。跨范式研究表明，个体间差异通常大于个体内波动，但两者均影响统计推断和模型泛化能力。稳定性具有特征依赖性：α波段指标及个体α峰值频率通常相对可靠，而高频成分及许多基于连接性的指标则表现出更异质的可靠性；ERP的可靠性因成分而异，其中P300测量常表现出中等至良好的稳定性。我们总结了变异性的主要来源（生物性、状态相关、技术性和分析性），回顾了常用的量化与建模方法（如ICC、CV、SNR、概化理论及多变量/基于学习的方法），并为研究设计、结果报告与数据协调提供了建议。总体而言，EEG变异性应被视为一个需要管理的实际约束，同时也是一个可利用的有意义信号，以推动精准神经科学与鲁棒神经技术的发展。