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

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 EEGProvides a step-by-step approach for designing clinical experiments using EEGIncludes example datasets for affected individuals and healthy controlsLists inclusion and exclusion criteria to help identify experiment subjectsFeatures appendices detailing subjective tests for screening patientsExamines applications for personalized treatment decisions

1. Introduction: Depression and Challenges
2. EEG Fundamentals
3. EEG-Based Brain Functional Connectivity and Clinical Implications
4. Pathophysiology of Depression
5. Using EEG for Diagnosing and Treating Depression
6. Neural Circuits and EEG Based Neurobiology for Depression
7. Design of EEG Experiment for Assessing MDD
8. EEG-based Diagnosis of Depression
9. EEG-based Treatment Efficacy Assessment Involving Depression