Multimodal Mutual Information Extraction and Source Detection with Application in Focal Seizure Localization

Current multimodal imaging–based source localization (SoL) methods often rely on synchronously recorded data, and many neural network–driven approaches require large training datasets, conditions rarely met in clinical neuroimaging. To address these limitations, we introduce MieSoL (Multimodal Mutual Information Extraction and Source Localization), a unified framework that fuses EEG and MRI, whether acquired synchronously or asynchronously, to achieve robust cross-modal information extraction and high-accuracy SoL. Targeting neuroimaging applications, MieSoL combines Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), leveraging their complementary strengths—MRI’s high spatial resolution and EEG’s superior temporal resolution. MieSoL addresses key limitations of existing SoL methods, including poor localization accuracy and an unreliable estimation of the true source number. The framework combines two existing components—Unified Left Eigenvectors (ULeV) and Efficient High-Resolution sLORETA (EHR-sLORETA)—but integrates them in a novel way: ULeV is adapted to extract a noise-resistant shared latent representation across modalities, enabling cross-modal denoising and an improved estimation of the true source number (TSN), while EHR-sLORETA subsequently performs anatomically constrained high-resolution inverse mapping on the purified subspace. While EHR-sLORETA already demonstrates superior localization precision relative to sLORETA, replacing conventional PCA/ICA preprocessing with ULeV provides substantial advantages, particularly when data are scarce or asynchronously recorded. Unlike PCA/ICA approaches, which perform denoising and source selection separately and are limited in capturing shared information, ULeV jointly processes EEG and MRI to perform denoising, dimension reduction, and mutual-information-based feature extraction in a unified step. This coupling directly addresses longstanding challenges in multimodal SoL, including inconsistent noise levels, temporal misalignment, and the inefficiency of traditional PCA-based preprocessing. Consequently, on synthetic datasets, MieSoL achieves 40% improvement in Average Correlation Coefficient (ACC) and 56% reduction in Average Error Estimation (AEE) compared to conventional techniques. Clinical validation involving 26 epilepsy patients further demonstrates the method’s robustness, with automated results aligning closely with expert epileptologist assessments. Overall, MieSoL offers a principled and interpretable multimodal fusion paradigm that enhances the fidelity of EEG source localization, holding significant promise for both clinical and cognitive neuroscience applications.

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