This research holds strong clinical importance as it addresses one of psychiatry’s most pressing challenges: the unpredictable and highly individualized response to depression treatments. By using noninvasive EEG signals and an efficient computational model, this approach could allow clinicians to predict, before treatment begins, whether a patient will benefit from SSRI or rTMS therapy. Predictive capability such as this could significantly reduce treatment delays, patient suffering, and suicide risk by eliminating ineffective therapeutic trials. The DPSB-based software provides an interpretable, data-driven decision aid for psychiatrists, complementing clinical judgment with quantitative neurophysiological insights. The authors note that while current validation was performed on modest sample sizes, future work will involve larger and more diverse populations, as well as extension to other treatments such as electroconvulsive therapy and ketamine. With its simplicity, speed, and high accuracy, the proposed model could be integrated into clinical EEG systems within the next several years, once further validation is achieved.