Drug-resistant epilepsy affects approximately 30% of the 50 million people living with epilepsy worldwide. Despite extensive research, seizure prediction remains a major challenge, with no system yet integrated into clinical practice. One of the main obstacles to progress lies in the number of a priori assumptions that researchers often introduce during the system creation process. These choices are frequently driven by practical constraints and empirical decisions rather than objective optimization and physiological data, potentially obscuring optimal solutions. This work presents a fully automated, data-driven toolbox designed to process both EEG and ECG signals while systematically optimizing key parameters to maximize final classification performance.