Department of Epileptology, University of Bonn, 53127 Bonn, Germany.
Department of Epileptology, University of Bonn, 53127 Bonn, Germany.
Sleep Med Rev. 2020 Dec;54:101353. doi: 10.1016/j.smrv.2020.101353. Epub 2020 Jul 9.
Recent years have witnessed a surge in human sleep electroencephalography (EEG) studies, employing increasingly sophisticated analysis strategies to relate electrophysiological activity to cognition and disease. However, properly calculating and interpreting metrics used in contemporary sleep EEG requires attention to numerous theoretical and practical signal-processing details that are not always obvious. Moreover, the vast number of outcome measures that can be derived from a single dataset inflates the risk of false positives and threatens replicability. We review several methodological issues related to 1) spectral analysis, 2) montage choice, 3) extraction of phase and amplitude information, 4) surrogate construction, and 5) minimizing false positives, illustrating both the impact of methodological choices on downstream results, and the importance of checking processing steps through visualization and simplified examples. By presenting these issues in non-mathematical form, with sleep-specific examples, and with code implementation, this paper aims to instill a deeper appreciation of methodological considerations in novice and non-technical audiences, and thereby help improve the quality of future sleep EEG studies.
近年来，人类睡眠脑电图（EEG）研究呈激增态势，研究人员采用日益复杂的分析策略，将电生理活动与认知和疾病联系起来。然而，要正确计算和解释当代睡眠 EEG 中使用的指标，需要注意许多理论和实际信号处理细节，这些细节并不总是显而易见的。此外，从单个数据集可以衍生出大量的结果测量指标，这增加了出现假阳性的风险，并威胁到可重复性。我们回顾了与 1）频谱分析、2）导联选择、3）相位和振幅信息提取、4）替代物构建以及 5）最小化假阳性相关的几个方法学问题，既说明了方法选择对下游结果的影响，也说明了通过可视化和简化示例检查处理步骤的重要性。通过以非数学形式、使用睡眠特定的示例并结合代码实现来呈现这些问题，本文旨在让新手和非技术受众更深入地了解方法学考虑因素，从而帮助提高未来睡眠 EEG 研究的质量。
