Table Of ContentEEG-BASED EXPERIMENT DESIGN
FOR MAJOR DEPRESSIVE
DISORDER
http://booksite.elsevier.com/9780128174203/
EEG-Based Experiment Design for Major Depressive Disorder
AAMIR SAEED MALIK and WAJID MUMTAZ
Resources available:
Includes links to useful reference articles, and real-world EEG data from depressed
patients and healthy controls
EEG-BASED
EXPERIMENT DESIGN
FOR MAJOR
DEPRESSIVE DISORDER
Machine Learning and
Psychiatric Diagnosis
A S M
AMIR AEED ALIK
Brain9D,Adelaide,Australia
W M
AJID UMTAZ
NewTechnologiesfortheInformationSociety(NTIS),
DepartmentofComputerScience,FacultyofAppliedSciences,
UniversityofWestBohemia,CzechRepublic
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Dedication
Dedicated to
Umama and Reham (from Aamir Malik)
To my parents, Dr. SaadiaIrfanand Abdul Hadi (fromWajid
Mumtaz)
About the Authors
Dr. Malik has a BS in Electrical Engineering from the University of
Engineering and Technology, Lahore, Pakistan, an MS in Nuclear
Engineering from the Quaid-i-Azam University, Islamabad, Pakistan,
and a second MS in Information and Communication and a PhD in
Information and Mechatronics from the Gwangju Institute of Science
and Technology, Gwangju, Korea. He has more than 20 years of
research experience and has worked for IBM, Hamdard University, the
Pakistan Government, Yeungnam University and Hanyang University
in Korea, and Universiti Teknologi PETRONAS in Malaysia. Currently,
he is a consultant in neurosignal and neuroimaging in Australia. He is a
fellow of IET and a senior member of IEEE. He is board member of
Asia Pacific Neurofeedback Association (APNA) and a member of the
Malaysia Society of Neuroscience (MSN). His research interests include
neurosignal and neuroimage processing and neuroscience big data ana-
lytics.Heistheauthoroffourbooksandanumberofinternational jour-
nal and conference papers with more than 2000 citations and a
cumulative impact factor of more than 200. He has a number of patents
and copyrights and has received numerousawards.
Dr. Wajid Mumtaz completed his PhD from the Center for Intelligent
Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS
(UTP), Malaysia in 2017. He continued as postdoctorate researcher from
2017 to 2018 at the same institution. Recently, he joined the University
of West Bohemia, Pilsen, Czech Republic, as a Postdoctoral Research
Fellow. He received his MS in computer engineering and his bachelor’s
degree in electrical engineering from the University of Engineering
and Technology, Taxila, Pakistan, in 2009 and 2005, respectively. His
researchinterestsinclude biomedical signalprocessing andapplications,
machine learning application to medical problem-solving such as diag-
nosis and treatment assessment, as well as adaptive noise cancellation
for real-world data such as electroencephalogram (EEG).
xi
Preface
This book provides a multidisciplinary resource for major depressive
disorder (MDD) also simply termed as depression. The main theme of
this book is to discuss the details of electroencephalography (EEG)-
based methods that can provide solutions to the diagnosis and
treatment-related issues for depression. The book addresses the
depression-related challenges by studying both neurobiological and
electrophysiological point of views. The book shows that MDD is a men-
tal illness and needs proper diagnosis and patient care. Hence, the
authors believe that this book can become a resource to increase aware-
ness against depression. In addition, the book may benefit clinical prac-
titioners, researchers, anddepressed patients.
The book introduces new treatment strategies for clinicians.
According to existing practice, the diagnosis of depression involves
structured clinical questionnaires such as the Beck Depression
Inventory (BDI) and Hospital anxiety and depression scale (HADS).
However, these questionnaires have a subjective nature and could be
inefficient in particular cases. Therefore, the investigation of objective
methodsinvolvingEEG technology could open new treatmentstrategies
for depression. Ultimately, improvement in the quality of life for
depressed patientispossible.
From the perspective of researchers, the book furnishes information
on an EEG-based experiment design involving depression. Thanks to
the digital age, the advancement in the implementation of machine
learning techniques has opened the door for ubiquitous applications,
and depression is not an exception. However, the idea is still in its
infancy,astheclinicalapplicationofEEG-based machine learning meth-
ods for depression still need research efforts to achieve clinical valida-
tion. Therefore, this book provides examples of EEG-based methods for
depression termed Intelligent Treatment management system (ITMS) for
depression. The ITMS exhibits improved machine learning solutions
and shows clinical relevance as well.
Chapter 1, Introduction: Depression and Challenges, introduces
depression and its subtypes and associated challenges. Moreover, the
chapter emphasizes the development of objective methods for diagnosis
and treatment efficacy assessments involving depression. In particular,
this chapter shows examples of the EEG research studies that have
xiii
xiv
PREFACE
addressedtheresearchareaonEEG-basedmethods for depressiondiag-
nosis and prognosis.
Chapter 2, Electroencephalography Fundamentals, elaborates on the
fundamentals of electroencephalography, including EEG frequency
bands, EEG recording techniques, EEG references, EEG artifacts, and
methods for EEG-based artifact reductions. The EEG artifact reduction
section explains the offline and online methods developed for multiple
channels and single channel EEG data. The references for each tech-
nique provide guidance for detailed information on each method.
Chapter 3, Electroencephalography-Based Brain Functional
Connectivity and Clinical Implications, discusses the EEG-based func-
tional connectivity (FC) methods and their clinical implications. In addi-
tion, the chapter tabulates various applications of the EEG-based
functional connectivity methods for mental illnesses such as MDD, epi-
lepsy, schizophrenia, alcohol-use disorder (AUD), and Alzheimer’s dis-
ease. Furthermore, this chapter furnishes a list of open-source
MATLAB-based toolboxes focused on developing EEG-based FC
methods.
Chapter 4, Pathophysiology of Depression, discusses the pathophysi-
ology of depression, especially detailing possible causes for depression
based on evidence provided by animal and human studies. The chapter
emphasizes fusing of information from different sources, including evi-
dence from human and animal studies and the functional level informa-
tion from the neuroimaging modalities. The underlying causes and
effects of depression include brain volume losses involving different
brain areas.
Chapter 5, Using Electroencephalography for Diagnosing and
Treating Depression, reviews research work involving EEG-based meth-
ods for diagnosis and treatment efficacy assessment for depression. The
chapter provides details on the EEG and event-related potential (ERP)
methods. The chapter explains common findingson thefusion of neuro-
biological and electrophysiological data. In addition, important research
gaps basedon this informationare highlighted.
Chapter 6, Neural Circuits and Electroencephalography-Based
Neurobiology for Depression, focuses on two main areas of study: (1)
the neural circuits implicated during depression and (2) the neurobiol-
ogy of EEG/ERP-based markers for depression. The first section
explains the underlying neural circuits, including important brain areas
implicated during depression. The purpose of the second section is to
discuss theories regarding the underlying mechanisms defining EEG
biomarkers for depression.
Chapter 7, Design of an Electroencephalography Experiment for
Assessing Major Depressive Disorder, provides a detailed design of the
EEG-based experiment involving issues such as the sample size
xv
PREFACE
calculations, study’s exclusion and inclusion criterion, experimental
setup for EEG/ERP data recording, and the selection of clinical ques-
tionnaires for depression. Moreover, the chapter provides information
of the study participants in tabular form, including their clinical scores
regarding disease severity, demographic information, and the total
number of participants included in the study based on the sample size
formula.
Chapters 8 and 9 provide a machine learning (ML) scheme, termed
Intelligent Treatment Management System (ITMS) for unipolar MDD
patients. Chapter 8, Electroencephalography-Based Diagnosis of
Depression, provides details of the EEG-based diagnosis for depression
(ITMS diagnosis) by classifying MDD patients and healthy controls.
Chapter 9, Electroencephalography-Based Treatment Efficacy
Assessment Involving Depression, explains EEG-based prediction of
treatment outcome of antidepressant therapy (ITMS treatment selection),
while classifying the MDD patients as either treatment respondents (R)
or nonrespondents (NR) based on the pretreatment EEG data acquired
from the MDD patients. The EEG-based scheme presented in these
chapters inherently involves subprocesses such as noise removal from
the EEG data, EEG-based feature extraction, feature selection, classifica-
tion, and validation, including 10-fold cross-validation (10-CV). This
chapter provides the technical details regarding these subprocesses.
All the chapters have summary and references at the end of each
chapter. The readers can use the references to get more detailed infor-
mation on a particular topic. We hope that readers will enjoy this book
and that the information provided will be useful for their practices and
research.
