Model selection for spectral parameterization.

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 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.