EEG中的跨受体和内受体变异性：系统调查
Inter- and Intra-Subject Variability in EEG: A Systematic Survey
AI总结：
本文系统研究了EEG在不同范式中的变异性，分析了生物、状态、技术及分析因素，并提出了研究设计和报告的建议。
摘要：
脑电图（EEG）是神经科学、临床神经生理学和脑机接口（BCIs）的基础，但显著的跨受体和内受体变异性限制了其可靠性、可重复性和应用。本文系统性地研究了在静息状态、事件相关电位（ERPs）和任务相关/BCI范式（包括运动想象和SSVEP）中，健康和临床群体中EEG变异性量化或建模的情况。在不同范式中，受体间差异通常大于受体内波动，但两者都影响推断和模型泛化。稳定性是特征依赖的：alpha波段测量和个体alpha峰值频率通常相对可靠，而高频和许多连接性衍生指标显示出更多异质性可靠性；ERP可靠性因成分而异，P300测量通常显示中等至良好的稳定性。我们总结了主要的变异性来源（生物学、状态相关、技术性和分析性），回顾了常见的量化和建模方法（例如ICC、CV、SNR、一般化理论和多变量/学习方法），并提供了研究设计、报告和协调的建议。总体而言，EEG变异性应被视为一种需要管理的实用限制，也是用于精确神经科学和稳健神经技术的有意义信号。
英文摘要：
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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