系统梳理脑电图在个体间和个体内变异规律，指导更可靠的神经科学研究与脑机接口设计。
Inter- and Intra-Subject Variability in EEG: A Systematic Survey
- 分析静息态、事件相关电位及任务态脑电的变异来源与量化方法。
- 发现α频段特征稳定，而高频频段和连接度指标变异大，P300成分可靠性中等以上。
- 提出研究设计与报告规范，适合神经科学与脑机接口领域研究人员参考。
脑电图（EEG）广泛应用于神经科学、临床神经生理学和脑机接口（BCIs），但显著的个体间和个体内变异限制了其可靠性、可重复性与转化应用。本文系统综述了健康与临床人群在静息态、事件相关电位（ERPs）及任务相关/BCI范式（包括运动想象和SSVEP）中脑电变异的量化与建模研究。结果显示，个体间差异通常大于个体内波动，两者均影响推断与模型泛化能力。稳定性具有特征依赖性：α频段指标及个体α峰值频率相对可靠，而高频段及多数连接度指标变异较大；不同ERP成分可靠性各异，其中P300常表现中等到良好稳定性。文中总结了主要变异来源（生物、状态、技术与分析因素），回顾常用量化与建模方法（如组内相关系数、变异系数、信噪比、广义可推广性理论及多变量/学习型方法），并提出研究设计、报告与标准化建议。总体而言，脑电变异既是需控制的现实约束，也是可用于精准神经科学与稳健神经技术的潜在信号。
原文摘要 · Abstract (English)
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.
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