Capturing Aperiodic Temporal Dynamics of EEG Signals through Stochastic Fluctuation Modeling

TL;DR

This paper introduces a new stochastic fluctuation model for EEG signals that captures their aperiodic, scale-invariant, and long-range dependent properties, offering deeper insights into brain dynamics and potential biomarkers.

Contribution

It proposes a novel framework with self-similarity and scale parameters to model EEG aperiodic activity, challenging existing models and enabling realistic signal reconstruction.

Findings

EEG fluctuations are self-similar and non-stationary.

The model accurately reproduces 1/f spectral profile and long-range dependency.

Proposes a new biomarker candidate beyond the 1/f slope.

Abstract

Electrophysiological brain signals, such as electroencephalography (EEG), exhibit both periodic and aperiodic components, with the latter often modeled as 1/f noise and considered critical to cognitive and neurological processes. Although various theoretical frameworks have been proposed to account for aperiodic activity, its scale-invariant and long-range temporal dependency remain insufficiently explained. Drawing on neural fluctuation theory, we propose a novel framework that parameterizes intrinsic stochastic neural fluctuations to account for aperiodic dynamics. Within this framework, we introduce two key parameters-self-similarity and scale factor-to characterize these fluctuations. Our findings reveal that EEG fluctuations exhibit self-similar and non-stable statistical properties, challenging the assumptions of conventional stochastic models in neural dynamical modeling. Furthermore, the proposed parameters enable the reconstruction of EEG-like signals that faithfully replicate the aperiodic spectrum, including the characteristic 1/f spectral profile, and long range dependency. By linking structured neural fluctuations to empirically observed aperiodic EEG activity, this work offers deeper mechanistic insights into brain dynamics, resulting in a more robust biomarker candidate than the traditional 1/f slope, and provides a computational methodology for generating biologically plausible neurophysiological signals.