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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
Graduate students in biological and biomedical sciences and engineering, neuroscientists, neurobiologists, biomedical engineers, post-doctoral fellows, researchers
- Introduction: Depression and Challenges
2. EEG Fundamentals
3. EEG-Based Brain Functional Connectivity and Clinical Implications
4. Pathophysiology of Depression
5. Using EEG for Diagnosing and Treating Depression
6. Neural Circuits and EEG Based Neurobiology for Depression
7. Design of EEG Experiment for Assessing MDD
8. EEG-based Diagnosis of Depression
9. EEG-based Treatment Efficacy Assessment Involving Depression
- No. of pages:
- © Academic Press 2019
- 17th May 2019
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr. Malik has a B.S. in Electrical Engineering from University of Engineering and Technology, Lahore, Pakistan, M.S in Nuclear Engineering from Quaid-i-Azam University, Islamabad, Pakistan, another M.S in Information & Communication and Ph.D in Information & Mechatronics from Gwangju Institute of Science & Technology, Gwangju, Korea. He has more than 15 years of research experience and has worked for IBM, Hamdard University, Government of Pakistan, Yeungnam University and Hanyang University in Korea. He is currently working as Associate Professor at Universiti Teknologi PETRONAS in Malaysia. He is fellow of IET and senior member of IEEE. He is board member of Asia Pacific Neurofeedback Association (APNA) and member of Malaysia Society of Neuroscience (MSN). His research interests include neuro-signal & neuro-image processing and neuroscience big data analytics. He is author of 3 books and a number of international journal and conference papers with more than 1000 citations and cumulative impact factor of more than 180. He has a number of patents, copyrights and awards.
Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia
Dr. Wajid Mumtaz has completed his PhD degree from Center for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Malaysia in 2017. He continued as post doctorate researcher from 2017 to 2018 from the same institution. Recently, he has joined University of West Bohemia, located in Pilsen, Czech Republic as a Postdoctoral Research Fellow. In addition, he accomplished his masters in computer engineering and bachelor in electrical engineering from University of Engineering and Technology, Taxila, Pakistan in 2009 and 2005, respectively. His research interest includes biomedical signal processing and applications, machine learning application to medical problem solving, such as diagnosis and treatment assessment, adaptive noise cancellation for real-world data, such as Electroencephalogram (EEG).
Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic