Eeg-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis
暫譯: 基於腦電圖的重度憂鬱症實驗設計：機器學習與精神診斷

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