Neural entrainment refers to the tendency of neural activity to align with an ongoing rhythmic stimulus. Measures of neural entrainment have been increasingly leveraged as a tool to understand how the brain tracks different types of regularities in sensory input. However, the methods used to quantify neural entrainment are varied, with numerous analytic decision points whose consequences have not been well-characterized. In a valuable contribution to this field, Benjamin, Dehaene-Lambertz and Flo (submitted) systematically compare various methodological approaches for studying neural entrainment. They demonstrate that the use of overlapping epochs, in which sliding time windows are extracted and analyzed, results in an artifactual inflation of entrainment estimates at the frequency of overlap. Here, in response to this updated best practice recommendation, we reanalyzed three previously published datasets that had been previously analyzed with overlapping epochs. Although our main results and conclusions are unaltered from those originally reported, we agree with Benjamin and colleagues that overlapping epochs should generally be avoided in classic analyses of steady-state experiments, which aim to quantify overall peaks in phase or power across an entire experimental duration. However, we present a case that overlapping epochs may be beneficial in fine-grained analyses of neural entrainment over time. The use of overlapping epochs in such analyses could improve temporal resolution without complicating interpretability of the results in cases where the question of interest relates to relative changes in neural entrainment over time within a given frequency.

神经夹带是指神经活动与正在进行的有节奏的刺激相一致的趋势。神经夹带的测量越来越多地被用作了解大脑如何跟踪感觉输入中不同类型规律的工具。然而，用于量化神经夹带的方法是多种多样的，具有许多分析决策点，其后果尚未得到很好的表征。Benjamin、Dehaene-Lambertz 和 Flo（已提交）系统地比较了研究神经夹带的各种方法学方法，对这一领域做出了宝贵贡献。他们证明，使用重叠时期（其中提取和分析滑动时间窗口）会导致重叠频率下夹带估计的人为膨胀。这里，为响应此更新的最佳实践建议，我们重新分析了之前发布的三个数据集，这些数据集之前曾用重叠的时期进行过分析。尽管我们的主要结果和结论与最初报告的结果和结论没有改变，但我们同意 Benjamin 及其同事的观点，即在稳态实验的经典分析中通常应避免重叠时期，这些分析旨在量化整个实验期间的相位或功率的总体峰值. 然而，我们提出了一个案例，即随着时间的推移，重叠的时代可能有利于神经夹带的细粒度分析。