EEG-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis

Introduces EEG-based machine learning solutions for diagnosis and assessment of treatment efficacy for a variety of conditions. With a unique combination of background and practical perspectives for the use of automated EEG methods for mental illness, it details for readers how to design a successful experiment, providing experiment designs for both clinical and behavioral applications.

This book details the EEG-based functional connectivity correlates for several conditions, including depression, anxiety, and epilepsy, along with pathophysiology of depression, underlying neural circuits and detailed options for diagnosis. It is a necessary read for those interested in developing EEG methods for addressing challenges for mental illness and researchers exploring automated methods for diagnosis and objective treatment assessment.

Written to assist in neuroscience experiment design using EEG

Provides a step-by-step approach for designing clinical experiments using EEG

Includes example datasets for affected individuals and healthy controls

Lists inclusion and exclusion criteria to help identify experiment subjects

Features appendices detailing subjective tests for screening patients

Examines applications for personalized treatment decisions