Introduction: Mapping brain activity from EEG signals helps researchers study how different brain regions respond during specific tasks. This mapping accuracy depends on the head model used to guide where the signals are coming from within the brain. Two popular types of models—Boundary Element Models (BEM) and Finite Element Models (FEM)—differ in complexity, with FEM providing more anatomical detail. Additionally, head models can either be individualized using each person's MRI or standardized using a template model. This study explores how these modeling choices affect the accuracy of EEG brain mapping. Methods: BEM and FEM head models were compared using both personalized and template-based setups. The models were first tested using simulated EEG data, after which they were applied to real EEG data collected during a face recognition task. Each model's performance was analyzed by measuring how accurately each model localized the sources of brain activity, looking at factors like precision and error rates. Results: FEM models, especially those based on individual MRIs, were most accurate in identifying brain areas responsible for EEG signals. Template models showed reduced accuracy, with BEM in particular producing broader and sometimes incorrect mappings. While template models are useful without individual MRIs, they carry a higher risk of errors in pinpointing exact brain regions. Discussion: These results suggest that while template models provide a practical alternative when individual MRIs are not available, subject-specific FEM models deliver the most reliable results for studies requiring precise localization. BEM models, though easier to compute, showed limitations in more complex brain regions. Conclusion: For EEG brain mapping, personalized FEM models offer the highest accuracy, making them ideal for studies requiring detailed brain region identification. Template-based BEM models, while convenient, should be used cautiously due to their lower precision, with implications for research in cognitive and clinical neuroscience.