Objective : The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals’ subjective insights. Incorporating 2-channel electroen-cephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. Methods : One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. Results : The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. Conclusion : Our subtype classification system offers a concise means to access individuals’ mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.