EEG-Based Experiment Design for Major Depressive Disorder
Machine Learning and Psychiatric Diagnosis
Häftad, Engelska, 2019
1 889 kr
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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.
- 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
Produktinformation
- Utgivningsdatum2019-05-17
- Mått152 x 229 x 20 mm
- Vikt450 g
- FormatHäftad
- SpråkEngelska
- Antal sidor254
- FörlagElsevier Science
- ISBN9780128174203