Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.

电生理信号表现出周期性和非周期性特性。周期性振荡与许多生理、认知、行为和疾病状态有关。新出现的证据表明，非周期性成分具有假定的生理学解释，并且它随着年龄、任务需求和认知状态而动态变化。通常使用规范定义的频带来分析电生理神经活动，而不考虑非周期性（1/f 类）分量。我们表明，标准分析方法可以将周期性参数（中心频率、功率、带宽）与非周期性参数（偏移、指数）混为一谈，从而损害生理学解释。为了克服这些限制，我们引入了一种算法，将神经功率谱参数化为非周期性分量和假定的周期性振荡峰值的组合。该算法不需要预先指定频带。我们在模拟数据上验证了该算法，并演示了如何将其用于从分析工作记忆中与年龄相关的变化到大规模数据探索和分析等应用中。