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

Author:   Aamir Saeed Malik (Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia) ,  Wajid Mumtaz (Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic) ,  Aamir Saeed Malik (Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia) ,  Aamir Saeed Malik (Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia)
Publisher:   Elsevier Science Publishing Co Inc
ISBN:  

9780128174203


Pages:   254
Publication Date:   17 May 2019
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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EEG-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis


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Author:   Aamir Saeed Malik (Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia) ,  Wajid Mumtaz (Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic) ,  Aamir Saeed Malik (Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia) ,  Aamir Saeed Malik (Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Perak, Malaysia)
Publisher:   Elsevier Science Publishing Co Inc
Imprint:   Academic Press Inc
Weight:   0.450kg
ISBN:  

9780128174203


ISBN 10:   012817420
Pages:   254
Publication Date:   17 May 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. 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

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Author Information

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. 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).

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