发表机构
Universitat Politècnica de Catalunya; Universitat de Girona（加泰罗尼亚理工大学; 赫罗纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过序数分析比较109名受试者闭眼与睁眼静息态EEG，验证了时间与空间排列熵在眨眼伪影、记录时长和电极数量变化下的稳健性，并发现二者均能显著区分两种状态。
AI 中文摘要
脑电图（EEG）是一种广泛用于监测大脑状态的非侵入性技术，许多研究致力于开发可靠且高效的EEG数据分析方法。在此，我们将序数分析应用于109名健康受试者在两种不同条件下测量的静息状态EEG记录：闭眼（EC状态）或睁眼（EO状态）。我们研究了时间排列熵（$PE$）和空间排列熵（$SPE$）相对于EO记录中眨眼伪影的存在、记录持续时间以及所分析的EEG通道数量的稳健性。我们进行了配对统计检验，以评估这些量在同一受试者的EO和EC记录中是否存在显著差异。我们发现$PE$和$SPE$表现得出奇地好，因为即使从原始信号中计算，它们也能在短时间内（$PE$）或从减少数量的电极（$SPE$）中检测到显著差异。
英文摘要
Electroencephalography (EEG) is a noninvasive technology that is widely used to monitor brain states, and many efforts are focused on developing reliable and efficient data analysis methods for EEG recordings. Here, we apply ordinal analysis to the EEG recording of the resting state of 109 healthy subjects measured in two different conditions: with eyes closed (EC state) or eyes open (EO state). We study the robustness of the temporal permutation entropy ($PE$) and the spatial permutation entropy ($SPE$) with respect to the presence of blinking artifacts in the EO recordings, the duration of the recordings, and the number of EEG channels analyzed. We perform a paired statistical test to assess whether these quantities differ significantly in the EO and EC recordings of the same subject. We find that $PE$ and $SPE$ perform surprisingly well, as they detect significant differences when calculated from the raw signals, even within a short time interval ($PE$) or from a reduced number of electrodes ($SPE$).
CommentsThis article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Gancio, Juan, et al. "Exploring the robustness of permutation entropy analysis to differentiate between closed-eyes and open-eyes resting states." Chaos (2026), and may be found at https://doi.org/10.1063/5.0326211
Journal refGancio, Juan, et al. "Exploring the robustness of permutation entropy analysis to differentiate between closed-eyes and open-eyes resting states." Chaos: An Interdisciplinary Journal of Nonlinear Science 36.6 (2026)
