Review on EEG and ERP predictive biomarkers for major depressive disorder

The selection of suitable antidepressants for Major Depressive Disorder (MDD) has been challenging andis mainly based on subjective assessments that include minimal scientific evidence. Objective meas-ures that are extracted from neuroimaging modalities such as electroencephalograms (EEGs) could bea potential solution to this problem. This approach is achieved by the successful prediction of antide-pressant treatment efficacy early in the patient’s care. EEG-based relevant research studies have shownpromising results. These studies are based on derived measures from EEG and event-related potentials(ERPs), which are called neurophysiological predictive biomarkers for MDD. This paper seeks to providea detailed review on such research studies along with their possible limitations. In addition, this paperprovides a comparison of these methods based on EEG/ERP common datasets from MDD and healthy con-trols. This paper also proposes recommendations to improve these methods, e.g., EEG integration withother modalities such as functional magnetic resonance imaging (fMRI) and magnetoencephalograms(MEG), to achieve better evidence of the efficacy than EEG alone, to eventually improve the treatmentselection process.