Eeg-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis by Aamir Saeed Malik - PDF and EPUB eBook

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

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Details of Eeg-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis

Exact title of the book
Eeg-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis
Book author
Aamir Saeed Malik
Book edition
Paperback
Number of pages
254 pages
Published
May 31st 2019 by Academic Press
File size (in PDF)
1016 kB
Eeg-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis

Some brief overview of book

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.