Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings

• Published in: Fractal and fractional Vol. 9; no. 5; p. 278
• Main Authors: Armonaite, Karolina, Conti, Livio, Laura, Luigi, Primavera, Michele, Tecchio, Franca
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2025
• Subjects: Artificial neural networks; Brain; cortical parcelling; Datasets; deep learning; Electroencephalography; Epilepsy; Fourier transforms; Fractal geometry; Fractals; Frequencies; Investigations; Methods; Neural networks; neurodynamics; Neurophysiology; Power spectral density; sEEG; Signal analysis; Time series; Wakefulness
• ISSN: 2504-3110; 2504-3110
• DOI: 10.3390/fractalfract9050278

Understanding human cortical neurodynamics is increasingly important, as highlighted by the European Innovation Council, which prioritises tools for measuring and stimulating brain activity. Unravelling how cytoarchitecture, morphology, and connectivity shape neurodynamics is essential for developing technologies that target specific brain regions. Given the dynamic and non-stationary nature of neural interactions, there is an urgent need for non-linear signal analysis methods, in addition to the linear ones, to track local neurodynamics and differentiate cortical parcels. Here, we explore linear and non-linear methods using data from a public stereotactic intracranial EEG (sEEG) dataset, focusing on the superior temporal gyrus (STG), postcentral gyrus (postCG), and precentral gyrus (preCG) in 55 subjects during resting-state wakefulness. For this study, we used a linear Power Spectral Density (PSD) estimate and three non-linear measures: the Higuchi fractal dimension (HFD), a one-dimensional convolutional neural network (1D-CNN), and a one-shot learning model. The PSD was able to distinguish the regions in α, β, and γ frequency bands. The HFD showed a tendency of a higher value in the preCG than in the postCG, and both were higher in the STG. The 1D-CNN showed promise in identifying cortical parcels, with an 85% accuracy for the training set, although performance in the test phase indicates that further refinement is needed to integrate dynamic neural electrical activity patterns into neural networks for suitable classification.