Description
| Product ID: | 9780128174203 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | NL |
| Title: | EEG-Based Experiment Design for Major Depressive Disorder |
| Subtitle: | Machine Learning and Psychiatric Diagnosis |
| Authors: | Author: Aamir Saeed Malik, Wajid Mumtaz |
| Page Count: | 254 |
| Subjects: | Psychiatry, Psychiatry |
| Description: | Select Guide Rating 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. |
| Imprint Name: | Academic Press Inc |
| Publisher Name: | Elsevier Science Publishing Co Inc |
| Country of Publication: | GB |
| Publishing Date: | 2019-05-17 |
