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

Malik, Aamir Saeed, Mumtaz, Wajid

  • 出版商: Academic Press
  • 出版日期: 2019-05-17
  • 售價: $4,350
  • 貴賓價: 9.5$4,133
  • 語言: 英文
  • 頁數: 254
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 012817420X
  • ISBN-13: 9780128174203
  • 相關分類: Machine Learning 機器學習
  • 下單後立即進貨 (約2~3週)

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商品描述

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