Product Detail
EEG-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis
Malik, Aamir Saeed
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Book Information
Edition:
1st
Publisher:
Academic Press, Incorporated
ISBN:
0-12-817420-X (012817420X)
ISBN-13:
978-0-12-817420-3 (9780128174203)
Binding:
Softcover
Copyright:
2019
Publish Date:
07/19
Weight:
0.91 Lbs.
Pages:
300
Subject Class:
NEU (Neurology and Neuroscience)
Return Policy:
Returns accepted up to 90 days provided no other recalls or return restrictions apply.
Contributing Authors:
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ProQuest Ebook Central:
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Class Specifications
Discipline:
Nervous Sys
Subject Definition:
Electroencephalography; Depression-Therapy
NLM Class:
WL 150
LC Class:
RC386.6
Abstract:
EEG-Based Experiment Design for Mental Illness: 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 pathophysiology of several conditions, including depression, anxiety, and epilepsy, along with 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 its diagnosis and objective treatment assessment. Written to assist in neuroscience experiment designs using EEGProvides a step-by-step approach in designing clinical experiments using EEGIncludes example datasets for affected individuals and healthy controlsLists inclusion and exclusion criteria to help identify experiment subjectsFeatures appendices detailing subjective tests for screening patientsExamines applications for personalized treatment decisions
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