Non-Invasive Seizure Onset Zone Localization Using Janashia–Lagvilava Algorithm–Based Spectral Factorization in Granger Causality

Background/Objectives: Precise identification of epileptic seizure onset zones (SOZs) and their propagation pathways is essential for effective epilepsy surgery and other interventional therapies, typically achieved through invasive electrophysiological recordings such as intracranial electroencephalography (EEG). Previous research has shown that analyzing information flow patterns, particularly in high-frequency oscillations (>80 Hz) using parametric and Wilson-algorithm (WL) based nonparametric Granger Causality (GC), is valuable for identifying SOZs. In this study, we analyzed scalp EEG recordings from epilepsy patients using an alternative nonparametric GC, which relies on spectral density matrix factorization based on the Janashia-Lagvilava algorithm (JLA). The aim of this study is to assess the effectiveness of JLA-based matrix factorization in nonparametric Granger causality for noninvasively identifying seizure onset zones from ictal EEG recordings in drug-resistant epilepsy patients. Methods: Two regions of interest in pairs across different time epochs were isolated in six people referred for presurgical evaluation. To apply the nonparametric Granger causality (GC) estimation approach to the EEG recordings from these regions, the cross-power spectral density matrix was first constructed by the multitaper method and then subsequently factorized by the JLA algorithm. The factorization gave a transfer function and noise covariance matrix needed for GC estimations. The GC estimates were obtained at different prediction time steps (measured in milliseconds). These estimates were used to confirm the visually suspected seizure onset regions and its propagation pathway. Results: JLA-based spectral factorization in Granger causality applied to scalp EEGs successfully identified seizure onset zones (SOZs) and their propagation patterns, aligning with positive outcomes (Engel I) in six epilepsy surgery cases. Conclusions: The JLA-based spectral factorization in Granger causality has the potential not only for accurately localizing SOZs to aid in diagnosis and treatment but also for broader applications in uncovering information flow patterns in neuroimaging and computational neuroscience.