Introducing the “ASCENT” EEGLAB Plugin: Aperiodic Signal Complexity Estimation for Neurophysiological Time Series

Entropy- and complexity-based measures are promising tools to characterize neural dynamics beyond traditional linear and oscillatory analyses. However, their adoption in electroencephalography (EEG) and magnetoencephalography (MEG) research has been limited by methodological fragmentation, computational cost, and a lack of standardized implementations and visualization tools. Hence, we developed ASCENT, an open-source EEGLAB plugin for computing and visualizing a broad range of entropy, fractal, and aperiodic measures in neurophysiological time series. ASCENT integrates established single-scale, multiscale, and multivariate entropy algorithms, fractal dimension estimators, and aperiodic spectral parameterization within an optimized framework, accessible through both a graphical interface and a fully parameter-tunable command-line interface. To our knowledge, it is the first toolbox to combine these measures on independent component (IC) time series and on scalp channels, and to offer time-resolved estimation of both modified multiscale entropy and aperiodic parameters. Outputs are stored in a structured format for statistical analysis with external tools, as illustrated by an accompanying group-analysis script. As a usage example, we applied ASCENT to resting-state EEG from 40 participants recorded with eyes closed and eyes open. Condition differences were found for multiple measures and temporal scales. Their direction, spatial distribution, and scale extent depended on the measure, the coarse-graining operator and, for the power spectrum, aperiodic correction. Correlation analyses helped to indicate which measures were redundant. ASCENT aims to lower the threshold for researchers to apply these measures consistently and interpret them appropriately.