Model selection for spectral parameterization

Abstract

AbstractNeurophysiological 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 challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. 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 and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.Lay summaryBrain 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 new, automated 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 new method is demonstrated with both synthesized and real-world data. The new approach is made available to all researchers as a free, open-source app, observing best practices for neuroscience research.