A novel algorithm is introduced to parameterize neural power spectra into periodic and aperiodic components, addressing limitations in traditional methods that conflate these components. The algorithm identifies oscillatory peaks based on their power above the aperiodic component, without requiring predefined frequency bands. It successfully recovers both periodic and aperiodic parameters in simulated data and matches human raters in identifying peak frequencies in EEG and LFP spectra. The algorithm is validated across various applications, including age-related differences in spectral parameters, working memory performance, and spatial analysis of periodic and aperiodic activity in resting state MEG data. Results show that age-related changes in oscillatory power can be influenced by shifts in oscillation center frequency and aperiodic exponent, and that aperiodic activity plays a significant role in predicting working memory performance. The algorithm also reveals spatial patterns of oscillatory and aperiodic activity across the human neocortex, highlighting the importance of parameterizing both components for accurate physiological interpretation. The method provides a principled approach to quantifying neural power spectra, disentangling periodic and aperiodic components to better understand neural dynamics and their relationship to cognitive, clinical, and physiological data.A novel algorithm is introduced to parameterize neural power spectra into periodic and aperiodic components, addressing limitations in traditional methods that conflate these components. The algorithm identifies oscillatory peaks based on their power above the aperiodic component, without requiring predefined frequency bands. It successfully recovers both periodic and aperiodic parameters in simulated data and matches human raters in identifying peak frequencies in EEG and LFP spectra. The algorithm is validated across various applications, including age-related differences in spectral parameters, working memory performance, and spatial analysis of periodic and aperiodic activity in resting state MEG data. Results show that age-related changes in oscillatory power can be influenced by shifts in oscillation center frequency and aperiodic exponent, and that aperiodic activity plays a significant role in predicting working memory performance. The algorithm also reveals spatial patterns of oscillatory and aperiodic activity across the human neocortex, highlighting the importance of parameterizing both components for accurate physiological interpretation. The method provides a principled approach to quantifying neural power spectra, disentangling periodic and aperiodic components to better understand neural dynamics and their relationship to cognitive, clinical, and physiological data.