This review surveys methods to separate oscillatory and aperiodic components of neural signals, arguing that aperiodic parameters are meaningful biomarkers linked to excitation–inhibition balance and neurodegeneration and that standardized metrics could inform closed-loop neuromodulation.

By proposing standardized estimation of aperiodic neural features and linking them to Parkinsonism and DBS modulation, the paper offers moderate translational value for developing reliable biomarkers and adaptive neuromodulation strategies, though it lacks direct molecular or therapeutic targets.

Neural activity encompasses both rhythmic oscillations and aperiodic background dynamics, reflecting complex brain function beyond traditional rhythm-centric views. The aperiodic component, 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 these periodic and aperiodic components. We critically evaluate evidence suggesting that aperiodic parameters 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 standardized roadmap for 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 (BCIs) and personalized neurotherapeutics.