Automated Biomarker Identification & Personalized Neurofeedback Calibration via Multi-Modal Data Fusion and Bayesian Optimization in Frontopolar Cortex Dysfunction

**Abstract:** This paper introduces a novel framework for automated biomarker identification and personalized neurofeedback calibration in individuals experiencing frontopolar cortex dysfunction. Leveraging advanced multi-modal data fusion techniques and Bayesian optimization, our system accurately identifies subtle physiological biomarkers correlated with cognitive deficits, and then dynamically calibrates neurofeedback protocols to specifically target and remediate these deviations. The core innovation lies in integrating EEG, fNIRS, and pupillometry data within a unified Bayesian framework, allowing for sensitive detection of subtle dysfunction and personalized treatment optimization previously unattainable. The system's immediate commercializability stems from its reliance on established neuroimaging technologies and Bayesian statistical methods, reducing both development costs and regulatory hurdles.