Abstract：
Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.
摘要：
电生理功率谱通常由两个分量组成：通常遵循1/f幂律[公式：见正文]的非周期性部分和作为谱峰出现的周期性分量。在调查周期性部分的同时，通常被称为神经振荡，受到了相当多的关注，非周期性部分的研究直到最近才获得更多的兴趣。周期部分通常由中心频率量化，权力，和带宽，而非周期性部分由y截距和1/f指数参数化[公式：见正文]。为了调查任何一部分，然而,重要的是要分开这两个组成部分。在这篇文章中,我们仔细研究了两种常用的方法，Foof（拟合振荡和One-Over-F）和IRASA（不规则重采样自动光谱分析），通常用于将周期性分量与非周期性分量分开。我们使用脑电图（EEG）获得的不同光谱来评估这些方法，脑磁图（MEG），和与三个独立研究数据集相关的本地场电位(LFP)记录。每种方法和每个数据集对提取两个光谱部分提出了不同的挑战。通过模拟强调这些特征的功率谱，突出了阻碍周期性和非周期性分离的特定光谱特征。通过与先验定义的仿真参数进行比较，量化了每种方法的参数化误差。根据真实和模拟的功率谱，我们评估了这两种方法的优势，讨论共同的挑战，注意哪些光谱特征阻碍了分离，评估计算成本，并就如何使用它们提出建议。
