Book Details
Orange Code:94731
Paperback:243 pages
Publications:
Categories:
Sections:
1. Introduction: Depression and Challenges2. Electroencephalography Fundamentals3. Electroencephalography-Based Brain Functional Connectivity and Clinical Implications4. Pathophysiology of Depression5. Using Electroencephalography for Diagnosing and Treating Depression6. Neural Circuits and Electroencephalography-Based Neurobiology for Depression7. Design of an Electroencephalography Experiment for Assessing Major Depressive Disorder8. Electroencephalography-Based Diagnosis of Depression9. Electroencephalography-Based Treatment Efficacy Assessment Involving Depression
Description:
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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.
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