Abstract: A method and system for determining a treatment outcome prediction for a patient using electroencephalograph (EEG) are disclosed, which include analyzing a brain region mutual interaction characteristics using scalp EEG data from a patient and obtaining a set of brain region mutual interaction feature matrices, performing a feature enhancement process to the brain region mutual interaction feature matrices to extract prominent mutual interaction features and applying a treatment response predictive model to determine a treatment outcome prediction for the patient. Further disclosed is a method of generating a treatment response predictive model using EEG dataset of a group of patients under a clinical treatment. The method and system can be used for determining a treatment outcome prediction for a psychiatric disorder patient and a patient diagnosed with COVID-19 and suffering from psychiatric disorder symptom(s) originated from COVID-19.

Abstract: A method and system for identifying cohorts of psychiatric disorder patients using electroencephalograph (EEG) are disclosed, which include analyzing a type of brain region mutual interaction characteristics using scalp EEG data from each of a group of patients and obtaining a set of brain region mutual interaction feature matrices, performing a feature enhancement process to the brain region mutual interaction feature matrices to extract prominent mutual interaction features, and applying a machine learning algorithm to identify cohorts of the group of psychiatric disorder patients. The method and system can also be used for patients diagnosed with COVID-19 and suffering from psychiatric disorder symptoms originated from COVID-19. The method and system can be used to identify responsive and non-responsive groups to a clinical treatment, different treatment outcome groups in response to a clinical treatment, or different risk groups to a clinical treatment.