Accurately localizing the seizure onset zone (SOZ) remains a significant challenge in epilepsy surgery, with variability among patients complicating surgical planning. While stereoelectroencephalography (SEEG) allows for precise monitoring, its success depends on strategic electrode placement, guided by clinicians’ implicit Bayesian reasoning. The Bayesian brain map (BBM) makes this implicit process explicit by providing a graphical user interface that visualizes probabilistic assessments of brain regions, allowing clinicians to update these assessments as new data become available. Preliminary findings suggest that targeting high-uncertainty regions enhances electrode placement efficiency, potentially reducing the total number of electrodes required. BBM can improve both pre- and post-invasive monitoring by integrating data from various modalities, supporting diagnostic decision-making. It facilitates strategic electrode placement by highlighting areas of uncertainty where additional data can reduce ambiguity, thereby improving SOZ localization. By reducing reliance on subjective judgment, BBM may promote consistent and reliable outcomes, making it a valuable tool for advancing epilepsy surgery.