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 the 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 how to design a successful experiment, providing experimental designs for both clinical and behavioral applications. In addition, the book details the pathophysiology of several conditions, including depression, anxiety and epilepsy, along with neural circuits and detailed options for diagnosis.

• 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