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

By: Aamir Saeed Malik and Wajid Mumtaz

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Synopsis: 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