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Eeg-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis
Aamir Saeed Malik; Wajid Mumtaz (Autor)
·
Academic Press
· Tapa Blanda
Eeg-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis - Aamir Saeed Malik; Wajid Mumtaz
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Reseña del libro "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 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 EEGProvides a step-by-step approach for 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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