Insights on neural signal analysis with Higuchi fractal dimension

Abstract

Abstract Neurophysiological signal analysis is crucial for understanding the complex dynamics of brain function and its deviations in various pathological conditions. Traditional linear methods, while insightful, often fail to capture the full spectrum of inherently non-linear brain dynamics. This review explores the efficacy and applicability of the Higuchi fractal dimension (HFD) in interpreting neurophysiological signals such as scalp electroencephalography (EEG) and stereotactic intracranial encephalography (sEEG). We focus on three case studies: i) distinguishing between Alzheimer’s disease (AD) and healthy controls; ii) classifying neurodynamics across diverse brain parcels looking for a signature of that cortical parcel; and iii) differentiating states of consciousness. Our study highlights the potential of non-linear analysis for deeper insights into brain dynamics and its potential for improving clinical diagnostics.