Abstract: Power spectral analysis is a common method in EEG/MEG data processing. In recent years, growing numbers of researchers have recognized that the aperiodic components of power spectra hold unique physiological significance and practical value. With the global adoption of toolkits such as SpecParam, the aperiodic analysis of resting-state EEG/MEG has garnered substantial attention. Here we provide a rapid-start guide for beginners in aperiodic analysis, offering tool comparisons and standardized workflows while synthesizing current research on the aperiodic activity of high-density resting-state EEG/MEG. Building on key findings from developmental neuroscience and neuropsychiatric disorders, we propose critical directions for advancing this field.
First, we systematically compare widely-used aperiodic analysis tools (e.g., SpecParam, IRASA) across some dimensions like spectral parameterization approaches, algorithmic foundations, and fitting parameter spaces. Using the representative SpecParam and sleep deprivation dataset, we then demonstrate a whole-brain standardized analysis protocol for high-density EEG/MEG studies. This framework addresses some current limitations in official tool tutorials that predominantly employ single-electrode examples, while highlighting the necessity for future multi-electrode spatial analyses and group comparison. Accompanying analysis code is provided in supplementary materials for replication.
Second, we consolidate major advancements of aperiodic analysis across neuroscience, psychology, and psychiatry. In developmental neuroscience, age-related aperiodic parameter flattening shows robust associations with cognitive decline and sleep deterioration. The aperiodic exponent emerges as a critical biomarker linking advanced cognitive functions, arousal states, and neurodevelopmental trajectories, offering electrophysiological insights into the behavioral mechanisms. In clinical psychiatry, significant aperiodic parameter alterations demonstrate diagnostic potential as the electrophysiological biomarkers for neuropsychiatric disorders. By disentangling periodic and aperiodic components through parameterization, this approach resolves previous contradictory findings while providing novel perspectives for assessing brain dysfunction. These applications underscore aperiodic analysis' cross-population validity and translational promise.
Finally, we identify three critical research frontiers: 1) Current methodologies insufficiently address whole-brain spatial distributions of aperiodic activity, necessitating spatial feature characterization to elucidate neurophysiological generation mechanisms; 2) Standardized analytical pipelines must be established across tools to enhance reproducibility; 3) The physiological interpretation of aperiodic parameters requires expansion through excitation-inhibition (E:I) balance theory, particularly via direct neurotransmitter association studies. These proposed directions aim to bridge existing gaps and propel systematic development of aperiodic analysis methodologies. Future research should integrate multimodal neuroimaging techniques, innovative experimental paradigms, and mechanistic modeling to strengthen the theoretical foundations and clinical applications of EEG/MEG aperiodic analysis.

Key words: aperiodic components, EEG/MEG, Power Spectrum, scale-free, resting-state