非周期图
先验与后验
偏移量(计算机科学)
光谱密度
组分(热力学)
算法
计算机科学
数学
物理
电信
热力学
认识论
组合数学
哲学
程序设计语言
作者
Thomas Donoghue,Matar Haller,Erik Peterson,Paroma Varma,Priyadarshini Sebastian,Richard Gao,Torben Noto,Antonio H. Lara,Jonathan D. Wallis,Robert T. Knight,Avgusta Y. Shestyuk,Bradley Voytek
标识
DOI:10.1038/s41593-020-00744-x
摘要
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.
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