Depression is a multifaceted mental illness that profoundly impacts individuals, their families, and society. Accurate diagnosis, outcome prediction, and treatment individualization are critical areas of clinical research in depressive disorders. Due to its non-invasive nature, electroencephalography (EEG) has been widely used to characterize alterations in the brain's electrical activity in individuals with depression.

Both linear and nonlinear EEG biomarkers improve diagnostic accuracy, particularly when combined with clinical evaluations and cognitive assessments, addressing depression's complexity.

EEG-derived measures are instrumental in predicting responses to pharmacological therapies, such as selective serotonin reuptake inhibitors (SSRIs), and non-pharmacological interventions, including non-invasive brain stimulation (NIBS) techniques like transcranial magnetic stimulation (TMS). These biomarkers enhance the detection of subtle neural changes associated with treatment efficacy. Moreover, machine learning (ML) and deep learning (DL) algorithms further optimize predictions by analyzing large EEG datasets and extracting hierarchical features, enabling robust models for therapy outcome forecasting.

By identifying patient-specific neural profiles, EEG indices are also applied in quantitative EEG (qEEG)-guided neurofeedback and personalized brain stimulation protocols. These approaches leverage unique neural patterns to achieve improved therapeutic outcomes.

This presentation highlights EEG as a mature and valuable tool for understanding the pathophysiology of depression and guiding tailored interventions for individuals with depressive disorders.