The paper introduces a novel algorithm for parameterizing neural power spectra into periodic and aperiodic components, addressing the limitations of traditional frequency band analyses. Standard approaches often conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), leading to misinterpretations of physiological phenomena. The algorithm does not require a priori specification of frequency bands and can identify oscillations based on their power above the aperiodic component. It is validated on simulated data and applied to real-world datasets, demonstrating its ability to capture both periodic and aperiodic parameters accurately. The algorithm is used to analyze age-related changes in working memory and to map spatial patterns of oscillations and aperiodic activity across the human neocortex. The results highlight the importance of explicitly parameterizing the aperiodic component, which has physiological relevance and can influence behavioral performance. The method provides a principled approach to quantifying neural power spectra, allowing researchers to better understand the rich variability in neural data and its underlying physiological mechanisms.The paper introduces a novel algorithm for parameterizing neural power spectra into periodic and aperiodic components, addressing the limitations of traditional frequency band analyses. Standard approaches often conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), leading to misinterpretations of physiological phenomena. The algorithm does not require a priori specification of frequency bands and can identify oscillations based on their power above the aperiodic component. It is validated on simulated data and applied to real-world datasets, demonstrating its ability to capture both periodic and aperiodic parameters accurately. The algorithm is used to analyze age-related changes in working memory and to map spatial patterns of oscillations and aperiodic activity across the human neocortex. The results highlight the importance of explicitly parameterizing the aperiodic component, which has physiological relevance and can influence behavioral performance. The method provides a principled approach to quantifying neural power spectra, allowing researchers to better understand the rich variability in neural data and its underlying physiological mechanisms.