A Bayesian Model-Selection Approach for Determining the Number of Spectral Peaks in Neural Power Spectra
Bibliographic record
Abstract
Abstract Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which includes selecting the correct maximum number of spectral peaks to fit to the spectrum. This challenges the replicability and robustness of findings. Here, we introduce a data-driven model-selection procedure for determining the appropriate number of oscillatory peaks to fit to neural power spectra, based on the Bayesian Information Criterion (BIC). We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral decompositions. Overall, the proposed spectral decomposition with data-driven model selection reduces reliance on user-defined ‘maximum number of peaks’ settings, enabling more robust, reproducible, and interpretable spectral parameterizations. Lay summary Brain activity is composed of rhythmic patterns that repeat over time and arrhythmic elements that are less structured. Recent advances in brain signal analysis have improved our ability to distinguish between these two types of components, enhancing our understanding of brain signals. However, current methods require users to adjust several parameters manually to obtain their results. The outcomes of the analyses, therefore, depend on each user’s decisions and expertise. To improve the replicability of research findings, the authors propose a revised method to streamline the analysis of brain signal contents. They developed a new algorithm that defines the parameters of the analytical pipeline informed by the data. The effectiveness of this method is demonstrated with both synthesized and real-world data. The approach is made available to all researchers as a free, open-source app, observing best practices for neuroscience research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".