Quantifying fractal and oscillatory components in neural signals for biomarker development

Neural activity encompasses both rhythmic oscillations and aperiodic background dynamics, reflecting complex brain function beyond traditional rhythm-centric views. Theaperiodiccomponent, once considered noise, is now recognised as a meaningful signal indicative of excitation-inhibition balance and intrinsic neural timescales. Here, we review advanced signal processing frameworks, including spectral parameterisation and burst detection algorithms, that disentangle theseperiodicandaperiodiccomponents. We critically evaluate evidence suggesting thataperiodicparameters track neurodevelopment and serve as candidate biomarkers for Alzheimer's Disease and Parkinsonism. Furthermore, we highlight how neuroengineering interventions, such as deep brain stimulation and acupuncture, actively modulate these features. Crucially, we address the current methodological heterogeneity in the field, proposing a standardisedroadmapfor estimation to resolve conflicting interpretations. These findings underscore the complementary roles of oscillatory and aperiodic dynamics, offering novel avenues for closed-loop brain-computer interfaces and personalized neurotherapeutics.