Distributing since 1889 • Employee Owned for Better Service

Your 1st Choice!

Quick Search:

Cart | My Account | View Printable Page

Product Detail

NOTICE: This is the ProQuest Ebook Centralï¿½ format of this title. What is ProQuest Ebook Centralï¿½?

Other formats:

All Formats

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

Malik, A

Pricing & Availability
Available: Yes*
This title does not qualify for any discount.

Other formats:

Book Information
Edition:	1st
Publisher:	Academic Press, Incorporated
ISBN:	0-12-817421-8 (0128174218)
ISBN-13:	978-0-12-817421-0 (9780128174210)
Binding:	E E Book + ProQuest Ebook Central
Copyright:	2019
Publish Date:	07/19
Weight:	0.00 Lbs.
Subject Class:	NEU (Neurology and Neuroscience)
Return Policy:	Non-Returnable.

ProQuest Ebook Centralï¿½:	Please sign in to preview this title

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

* Subject to ProQuest Ebook Centralï¿½ availability

Follow Matthews Book Co. on: