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.

1. Introduction: Depression and Challenges2. EEG Fundamentals3. EEG-Based Brain Functional Connectivity and Clinical Implications4. Pathophysiology of Depression5. Using EEG for Diagnosing and Treating Depression6. Neural Circuits and EEG Based Neurobiology for Depression7. Design of EEG Experiment for Assessing MDD8. EEG-based Diagnosis of Depression9. EEG-based Treatment Efficacy Assessment Involving Depression

Subject Areas: Neurosciences [PSAN], Science: general issues [PD]