Influence of Patient-Specific Head Modeling on EEG Source Imaging

• Published in: Computational and mathematical methods in medicine Vol. 2020; no. 2020; pp. 1 - 15
• Main Authors: Céspedes-Villar, Yohan, Castellanos-Dominguez, German, Martinez-Vargas, Juan David
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi
• Subjects: Adolescent; Brain - anatomy & histology; Brain - diagnostic imaging; Brain Mapping - methods; Brain Mapping - statistics & numerical data; Child; Child, Preschool; Computational Biology; Electroencephalography - methods; Electroencephalography - statistics & numerical data; Electromagnetic Phenomena; Head - anatomy & histology; Head - diagnostic imaging; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Magnetic Resonance Imaging - statistics & numerical data; Models, Neurological; Neuroimaging - statistics & numerical data; Patient-Specific Modeling - statistics & numerical data
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2020/5076865

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject’s head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.