Magnetic resonance imaging (MRI)-based electroencephalography (EEG) forward modeling method has become prevalent in the field of EEG. However, due to the inability to obtain clear images of an infant’s fontanel through MRI, the fontanelle information is often lacking in the EEG forward model, which affects accuracy of modeling in infants. To address this issue, we propose a novel method to achieve fontanel compensation for infant EEG forward modeling method. First, we employed imaging segmentation and meshing to the head MRIs, creating a fontanel-free model. Second, a projection-based surface reconstruction method was proposed, which utilized priori information on fontanel morphology and the fontanel-free head model to reconstruct the two-dimensional measured fontanel into a three-dimensional fontanel model to achieve fontanel-compensation modeling. Finally, we calculated a fontanel compensation-based EEG forward model for infants based on this model. Simulation results, based on a real head model, demonstrated that the compensation of fontanel had a potential to improve EEG forward modeling accuracy, particularly for the sources beneath the fontanel (relative difference measure larger than 0.05). Additional experimental results revealed that the uncertainty of the infant’s skull conductivity had the widest impact range on the neural sources, and the absence of fontanel had the strongest impact on the neural sources below the fontanel. Overall, the proposed fontanel-compensated method showcases the potential to improve the modeling accuracy of EEG forward problem without relying on computed tomography (CT) acquisition, which is more in line with the requirements of practical application scenarios.
