[1]

[cor1]Corresponding author: Associate professor. Dr. Alice OTHMANI

# EEG based Major Depressive disorder and Bipolar disorder detection using
Neural Networks:A review

Sana Yasin  Syed Asad Hussain  Sinem Aslan  Imran Raza  Muhammad Muzammel
Alice Othmani  Department of Computer Science, COMSATS University Islamabad,
Lahore Campus Lahore,Pakistan  Department of Computer Science, University of
Okara, Okara Pakistan  Ca’ Foscari University of Venice, DAIS & ECLT, Venice,
Italy  Ege University, International Computer Institute, Izmir, Turkey
Université Paris-Est Créteil (UPEC), LISSI, Vitry sur Seine 94400, France
<EMAIL_ADDRESS>
###### Abstract

Mental disorders represent critical public health challenges as they are
leading contributors to the global burden of disease and intensely influence
social and financial welfare of individuals. The present comprehensive review
concentrate on the two mental disorders: Major depressive Disorder (MDD) and
Bipolar Disorder (BD) with noteworthy publications during the last ten years.
There is a big need nowadays for phenotypic characterization of psychiatric
disorders with biomarkers. Electroencephalography (EEG) signals could offer a
rich signature for MDD and BD and then they could improve understanding of
pathophysiological mechanisms underling these mental disorders. In this
review, we focus on the literature works adopting neural networks fed by EEG
signals. Among those studies using EEG and neural networks, we have discussed
a variety of EEG based protocols, biomarkers and public datasets for
depression and bipolar disorder detection. We conclude with a discussion and
valuable recommendations that will help to improve the reliability of
developed models and for more accurate and more deterministic computational
intelligence based systems in psychiatry. This review will prove to be a
structured and valuable initial point for the researchers working on
depression and bipolar disorders recognition by using EEG signals.

###### keywords:

Electroencephalogram(EEG) Major Depressive disorder(MDD) Bipolar disorder(BD)
Artificial neural networks biomedical informatics

A summary of clinical background of MDD and BD, Brain structure and EEG based
brain activity,

EEG experimental protocols study for mental disorders diagnosis,

An extensive study of state-of-the-art shallow and deep neural networks models
for EEG based clinical depression detection,

An extensive study of neural networks based approaches for Biploar Disorder
diagnosis using EEG signals,

A discussion of state-of-the-art methods limitations and giving valuable
recommendations for future research

##  1 Introduction

Mental disorders represent critical public health challenges because they are
a leading contributors to the global burden of disease and profoundly impact
social and economic welfare of the people. The World Health Organization
predicted that by the year 2020, mental disorders shall be the first cause of
disability worldwide 111https://www.who.int/news-room/fact-
sheets/detail/mental-disorders. According to WHO, it is predicted that over
264 million women and men of all ages bears some form of mental disorder,
indicating that mental health problems effect up to 27% of the general
population at some point during their lives
222https://www.healthline.com/health/depression/effects-brain##1. The total
cost of mental, neurological, and substance use (MNS) disorders in the United
States is over 210 billion, mostly related to indirect costs [1]. MNS
disorders, therefore, account for 35% of the overall burden of illness and are
more costly than the combined burden of diabetes and cancers [2]. In contrast
to other fields of medicine, psychiatry is still plagued by two problems: (1)
a classification of mental disorders333International Classification of
Diseases or the Diagnostic and Statistical Manual of Mental Disorders (DSM):
www.who.int/substance_abuse/terminology/diagnostic/en based on clinical
symptoms of overlapping nosographic entities rather than on causal factors;
(2) a pharmacologic arsenal that only targets clinical symptoms, mostly in an
incomplete manner in a majority of patients. Moreover, mapping diagnostic
labels from a clinically defined nosology to varying biological indices has
proven to be problematic [3]. Therefore, we need Mental Health Innovation and
new ways to diagnose mental diseases by finding new biomarkers. Artificial
Intelligence (AI) can play a key role in the psychiatry revolution. Multimodal
Artificial Intelligence-based approaches and technologies need to be developed
in order to advance our understanding and care of mental health, improve early
precise diagnosis and prognosis, develop innovative treatments, and develop
assistive technologies for longitudinal follow-up of the patient.

Recently, automatic recognition of mental states and mental disorders has
attracted considerable attention from computer vision and artificial
intelligence community. While, earlier works mostly adopted Functional
Magnetic Resonance Imaging (fMRI) [4], visual cues [5], self-rating scale [6]
and social network analysis [7]. In recent years non-invasive sensors based
devices, such as Electroencephalogram (EEG), have been widely employed in the
literature. One of the most remarkable research efforts has been made on
developing efficient neural networks-based approaches for EEG signals analysis
for automatic assessment of mental disorders such as Major Depressive Disorder
(MDD) or Bipolar Disorder (BD).

Electroencephalogram (EEG) is a non-invasive, effective, and powerful tool for
recording the electrical activity of the brain and for the diagnosis of
various mental disorders such as MDD [8], BD [9], anxiety [10], schizophrenia
[11], and sleep disorders [12]. Due to these mental disorders or anomalies
specifically depression and bipolar disorder, the body releases cortisol to
the brain which affects the neurons production and communication and
consequently slowing down the functionality of some parts of brain and
changing the electrical activities patterns. The resulting voltage variations
from ionic current flows within the neurons of the brain could help in the
diagnosis of mental disorders like depression and bipolar disorder [13]. The
development of robust approaches for brain signals analysis is a challenging
task because of the complexity, the unstructured nature of signals and the big
variability related to the person, to its age and its mental health. Moreover,
brain signals are frequently affected by different types of noise due to eye
blinking and muscular activities during EEG recording [14].

Major Depressive Disorder and Bipolar Disorder are mental disorders that
affect physical health, sleep, appetite, attention level and they can lead to
suicidal thoughts or actions. Both disorders have many similarities, but they
also have some crucial differences. Patients with Bipolar disorder experience
high mood swings that include excessive highs (mania or hypomania) and lows
(depressive episodes). In mania episode, patients feel overactive, full of
energy and usually irritable. While they feel opposite during depressive
episodes with a strong feeling of hopeless and loss of pleasure in most of
activities. Further, in depressive episodes they do not experience any
elevated and extreme feelings that mania or hypomania patient face [15].

The diagnosis of bipolar disorder is not always easy because many of the
symptoms overlap with depression [16]. Bipolar patients most probably consult
their doctor for the first time when they have a depressive episode, instead
of during a manic or hypo-manic episode. Due to this reason, clinicians
frequently misdiagnose bipolar disorder as depression [16].

In this paper, we present an exhaustive review of existing neural networks-
based approaches, i.e., both shallow and deep architectures, for _Major
Depressive Disorder_ (MDD) and _Bipolar Disorder_ (BD) diagnosis using EEG
signals. With this review, we aim to provide a head start to the researchers
with an up-to-date survey of advances of EEG based depression and bipolar
disorders detection techniques for further contributions in this field. To the
best of our knowledge, only a few surveys related to depression and EEG
signals analysis, i.e., [17, 8, 18, 19, 6, 20], have been published on
depression diagnosis based on machine learning [17], computer-aided diagnosis
(CADx) [8], speech analysis techniques[18], nutrition examination [19], self-
rating scale [6] and by smartphone application [20]. Some of the surveys [21,
22, 23, 24, 25] cover the literature works on deep learning-based approaches
using EEG signals. However, these techniques do not focus on depression
diagnosis rather they consider other cognitive tasks [23, 24], such as
classification of brain signals [22], motor imagery [25], and Brain Computer
Interfaces (BCI) [21]. To the best of our knowledge, this paper is the first
comprehensive survey on neural network approaches that adopt either shallow or
deep neural networks and using EEG signals for MDD and BD detection.

###  1.1 Our Contributions

In the past studies, deep learning and mental disorders fields have been
studied separately. Just a few years ago, crossovers between these two areas
have been merged and researchers have used deep learning for EEG-based mental
disorders detection. Table 1 shows the existing surveys related to deep
learning, Electroencephalogram (EEG) and mental disorders. To the best of our
knowledge, this review is the first comprehensive study of the latest
improvements and front lines of deep learning and artificial neural networks
for MDD and BD recognition. In this survey, we have considered papers, most of
which has been published in the last five years (since 2015). The
contributions of this survey are as follows:

  1. 1.

A systematic review of artificial neural networks including shallow and deep
learning-based approaches to detect Major depressive disorder (MDD) and
Bipolar disorder using EEG, is presented to provide researchers an extensive
understanding of this area of research.

  2. 2.

Discussion on standard deep learning techniques and state-of-the-art models
for MDD and BD detection, and providing some guidelines for choosing the
suitable deep learning models.

  3. 3.

A review of applications and challenges of deep learning and ANN-based MDD and
BD detection. It also highlights some fascinating topics for future research.

Table 1: Summary of existing surveys and reviews related to DL, EEG,MDD and BD where DL stands for deep learning, EEG stands for Electroencephalogram, MDD stands for Major depressive disorder and BD stands for Bipolar disorder. Publication |  Years |  Topic | Scope
---|---|---|---
|  |  |  DL |  EEG |  MDD |  BD
[26] |  2017 |  A survey on deep learning |  ✓ |  x |  x |  x
[27] |  2018 |  A survey on deep learning |  ✓ |  x |  x |  x
[28] |  2019 |  A survey on deep learning |  ✓ |  x |  x |  x
[29] |  2018 |  A survey on deep learning |  ✓ |  x |  x |  x
[30] |  2019 |  A survey on deep learning |  ✓ |  x |  x |  x
[31] |  2013 |  A survey on EEG |  x |  ✓ |  x |  x
[32] |  2020 |  A survey on EEG |  x |  ✓ |  x |  x
[33] |  2020 |  A survey on EEG |  x |  ✓ |  x |  x
[34] |  2019 |  A survey on EEG |  x |  ✓ |  x |  x
[35] |  2020 |  A survey on EEG |  x |  ✓ |  x |  x
[36] |  2014 |  A survey on depression |  x |  x |  ✓ |  x
[37] |  2017 |  A survey on depression |  x |  x |  ✓ |  x
[7] |  2020 |  A survey on depression |  x |  x |  ✓ |  x
[38] |  2020 |  A survey on depression |  x |  x |  ✓ |  x
[3] |  2019 |  A survey on depression |  x |  x |  ✓ |  x
[21] |  2019 |  A survey on deep learning and EEG |  ✓ |  ✓ |  x |  x
[22] |  2018 |  A survey on deep learning and EEG |  ✓ |  ✓ |  x |  x
[23] |  2018 |  A survey on deep learning and EEG |  ✓ |  ✓ |  x |  x
[24] |  2019 |  A survey on deep learning and EEG |  ✓ |  ✓ |  x |  x
[25] |  2020 |  A survey on deep learning and EEG |  ✓ |  ✓ |  x |  x
[39] |  2020 |  A survey on bipolar disorder |  x |  x |  x |  ✓
[40] |  2020 |  A survey on depression and bipolar disorder |  x |  x |  ✓ |  ✓
[41] |  2009 |  A survey on EEG based bipolar disorder |  x |  ✓ |  x |  ✓
Our Review |  2019 |  Survey on EEG based MDD and BD detection using neural network |  ✓ |  ✓ |  ✓ |  ✓

###  1.2 Survey Organization

The current review is organized into nine sections. Section 2 covers search
strategy and eligibility criteria. Section 3 describes the
Electroencephalogram and brain structure. The section 4 elaborates the
clinical background of depression and summarizes the assessment of clinical
depression by verbal and nonverbal signs. Section 5 reviews the neural
networks-based approaches, EEG experimental protocols and public datasets for
depression recognition, while Section 6 presents a clinical background of
bipolar disorder by verbal and nonverbal signs. Section 7 encloses the
information about the neural networks-based approaches and EEG experimental
protocols for bipolar disorder recognition. The discussion of the current
review findings and suggestions for future studies are given in Section 8. At
the end conclusion in section 9 revealed the potential of shallow and deep
neural network for depression and bipolar disorder recognition.

##  2 Search Strategy and Eligibility Criteria

We have searched IEEEXplore, PubMed, Embase, Springer, ScienceDirect and Web
of Science, for articles published between January 2010, and May 2020 by using
the following keywords: (“Depression” OR “shallow neural network” OR “Deep
learning” OR “Electroencephalogram” OR “Cross-validation” OR “Bipolar
depression” OR “Artificial neural network” OR “uni polar depression” OR “EEG
base depression” OR “bipolar depression” OR “Major depressive disorder” OR “”
OR “Recurrent neural network” OR “Deep neural network OR “BDI-II” OR “DSM-IV”
OR “PHQ-9” OR “Persistent depressive disorder” OR “Deep Learning Models” OR
“EEG bio-markers” OR “Deep Feature Extraction” OR “FFNN OR Feed forward neural
network” OR “FBNN OR Feed backword neural network” OR “Discriminatives Deep
learning models” OR “Representative Deep learning Models” OR “Generative Deep
Learning Models” OR “Hybrid Deep learning Models” OR “Depression diagnosis’s
techniques” OR “Convolutional neural network OR CNN” OR “Mild Depression” OR
“MLP” OR “Mental States” OR “ EEG Artifacts” OR “Bipolar Depression” OR “Manic
Disorder” OR “Deep Belief Networks” OR “LSTM OR Long Short Term Memory. We
also explored the articles that cite the ones that we found by the key-words
mentioned above. There were no language restrictions.

We performed this systematic review by conforming to the PRISMA statement [42]
that helps to improve the reporting of systematic reviews and meta-analyses.
Eligibility criteria of this review includes the suitable depictions of
different shallow and deep neural network techniques for the automatic
assessment of depression by using EEG and representation of scientifically
acquired data and generation of real-time results. Different technical reports
and procedures [43], [44] of systematic reviews are followed to complete this
survey. Publication dates of the EEG based depression studies meeting the
criteria of ten years is considered. Only those subjects are included in this
survey that have more than 13 depression severity scores and no prior history
of drug and medication. The main aim of this review is to sum up all the MDD
and BD detection techniques that are performed by the shallow and neural
network. The Inclusion criteria for the current review encloses 1) adequate
depiction of EEG and neural networks based automatic depression assessment
classifiers and 2) demonstration of scientifically derived data, generating
concrete and accurate results. Strictly clinical studies, as well as
depression detection approaches relying on other techniques than EEG, are not
included. The keywords used to search electronic databases and related
resources are listed in Table 2. These keywords were used interchangeably, in
combinations of two or more, with either “OR” or “AND” operands.

Table 2: Keywords and web resources used in the current review. Web Resources |  Theme |  Keywords
---|---|---
Elsevier |  |  Major Depressive Disorder
Springer |  |  MDD
Google Scholar |  Depression |  Clinical Depression
Mayo Clinic |  |  Unipolar disorder
PubMed |  |  Mild Depression
Scopus |  |  BD
ACM Digital Library |  |  Mania
American Psychiatric Association |  Bipolar Disorder |  Manic depression
Embase |  |  Hypomania
NASA |  |  Manic-depressive illness
Web of Science |  |  Shallow and deep neural network
IEEE Xplore Digital Library |  |  FFNN or Feed forward neural network
World Health Organization |  Deep Learning |  FBNN or Feed backword neural network
|  |  CNN or Convolutional neural network
|  |  RNN or Recurrent neural network
|  |  EEG based depression
|  Electroencephalogram |  EEG-based unipolar depression
|  |  EEG based bipolar disorder
|  |  EEG biomarkers

##  3 Electroencephalogram (EEG) and Brain Structure:

Electroencephalogram (EEG) is an electro-biological measurement method that
records the electrical activity of the brain signals that are highly random
and encloses valuable information about the brain parts [45]. It records
brain’s electrical activity over sometime by providing non-invasive and cost-
effective solutions [46]. It is extensively used by physicians and researchers
to study brain functions and to detect neurological syndromes. Due to the
reliability factor of EEG, it is also utilized in EEG Biometrics for Person
Verification [47]. Moreover, among different depression detection techniques
(like audio [48], facial [49], text [50] and MRI [4]) EEG method is the most
reliable due to its ease of use (i.e., it requires a simple placement of
electrodes) and its high temporal resolution.

Depression and bipolar anomalies usually indicate dysfunction in the human
brain. Abnormal shape of EEG signals appears as variations in the signals
patterns for particular states of the patient and EEG reacts to the biotic
activities of the brain, for the accurate detection of the brain abnormalities
[46]. Both normal and depressed EEG signals are disordered and composite in
nature with refined differences reflecting different brain activities of the
depressive and normal groups that cannot be formulated easily through visual
interpretations.

###  3.1 Background of Electroencephalogram

The first human EEG recording was performed in 1924 by Hans Berger, who was a
neuro psychiatrist at the University of Jena Germany. He gives the German name
“elektrenkephalogramm” to EEG device that represents the graphical
representation of the electric currents generated in the brain. Further, he
presents that currents in the electrical pulses of the brain changes with
respect to the functional status of the brain, such as, sleep, epilepsy and
anesthesia. This idea of Hans Berger revolutionize the medical field and thus
helped to open a new branch of medical science called neurophysiology. EEG
signals can be divided into five different categories based on their bandwidth
i.e., alpha and beta, theta, delta and gamma, as illustrated in Fig 1(a).
Alpha and beta waves can be used to represent conscious states; while, theta
and delta waves are mostly used to represent unconscious states [51]. Gamma
rhythm has been attributed to sensory perception [52]. The frequency ranges
and major characteristics of brain waves are given in Table 3.

EEG signals can be recorded using invasive and non-invasive approaches. In an
invasive approach, EEG electrodes are implanted in the brain by surgery or can
be implanted at forehead sites. The manufacturing costs of these invasive
electrodes are very high. While, the non-invasive electrodes are positioned on
the scalp [46]. In a non-invasive approach, EEG signals can be recorded by
using two types of electrodes, i.e., 1) wet electrodes and 2) dry electrodes.
Wet electrodes are often made of silver chloride (AgCl) and use a gel to
create a conductive path between the electrodes and the skin by reducing the
impedance value. The gel leakage can cause a short circuit between different
electrodes. Furthermore, the extensive use of gel can cause allergy or any
other infection. Therefore, non-invasive dry electrodes are also proposed to
measure the EEG signals. These electrodes do not require gel or any skin
penetration. Also, these electrodes work perfectly even on the hairy sites
[53, 54, 55, 56, 57].

Table 3: Electroencephalogram (EEG) bands and their characteristics. EEG Waves |  Frequency Ranges(Hz) |  Brain States |  Mostly founded
---|---|---|---
Delta waves |  0.1-3Hz |  Unconscious/Sleeping |  Newborns and deep sleep phases
Theta Waves |  4-8Hz |  Imagination |  drowsiness and sleep
Alpha waves |  8-13 Hz |  Relaxed/Conscious |  normal and relaxed subjects
Beta waves |  13-30Hz |  Conscious/Focused/ Problem solving |  attentive or nervous subjects
Gamma waves |  30-40Hz |  Conscious perception/ Peak performance |  Attentive subjects

###  3.2 Brain Structure

The Human brain is an incredible part of the body that controls all the body’s
functions and interprets the information from the outside world. It is
composed of cerebrum, cerebellum and brainstem that is enclosed within the
skull. The cerebrum is a major part of the brain. It accomplishes complex
functions like inferring touch, visualization, hearing, speaking, cognitive,
sensations, learning, and adequate control of movement. The cerebrum is made
of left and right hemispheres, that controls the opposite side of the body and
have distinctive fissures. Each hemisphere has four lobes: frontal, temporal,
parietal, and occipital that are illustrated in Fig. 1(b).

Each lobe represents different information of the human brain, i.e., frontal
lobe controls the consciousness, the temporal lobe is responsible for the
computation of complex stimuli and the senses of smell and sound, the parietal
lobe represents the sensual information and the management of objects while
the occipital lobe provides information about the sense of sight. To extract
the information about the depressive and non depressive subjects, EEG
electrodes are placed at different lobes (frontal, temporal, parietal, and
occipital) of the cortex, as can be seen in Fig. 1(c). The placement of the
electrodes at the scalp is important, because different lobes of the cerebral
cortex are responsible for giving the information of electrical activities of
the brain by mono polar and bipolar recordings [57]. Mono polar recording
extracts the voltage variance among a reference electrode on the ear lobe and
an electrode on the scalp. In contrast, Bipolar electrodes gathers the voltage
variance among two scalp electrodes.

(a) EEG waves (Adapted from[58]).

(b) Representation of Brain Lobes (Adapted from [59]).

(c) Birds eye view of electrode placement (Adapted from [60]).

Figure 1: a)Brain structure and EEG waves representation: b) Different brain
lobes and their location; b) The placement of EEG electrodes at four brain
lobes using 10-20 international system.

###  3.3 Effect of Depression and Bipolar Disorder on Brain Structure

Depression and Bipolar Disorder affects three portions of the brain:
hippocampus (resides in the temporal lobe of the brain), prefrontal cortex
(located at the front of the frontal lobe) and amygdala (the frontal portion
of the temporal lobe) [61]. The hippocampus holds memories and controls the
production of a hormone called cortisol. During depression body releases
cortisol; that becomes problematic when its excessive amount is released and
sent to the brain. People with MDD, face long-term exposure of increased
cortisol levels which can slow down the production of new neuron. It also
causes the neurons in the hippocampus to shrink, hence leads to memory
problems.

The prefrontal cortex that resides in the frontal lobe is responsible for
controlling emotions, making decisions, and creating memories. When the amount
of cortisol exceeds in the brain, the prefrontal cortex gets shrink. The
amygdala exists in the frontal portion of the temporal lobe and it enables
emotional responses. In depression and bipolar disorder patients, the amygdala
becomes large and more vigorous due to the continuous exposure of a high ratio
of cortisol. An enlarged and manic amygdala, with irregular activity in other
portions of the brain, can consequence in sleep disorders and other activity
patterns. Usually, cortisol levels increase in the morning and reduce at
night. However, in MDD patients cortisol ratio is always higher even at night.
A Literature survey at EEG based depression and bipolar disorder detection
represented in Table 9 of Section 3 shows that the frontal lobe is more
effected by depression compared to the other brain lobes.

##  4 Clinical Background of Depression

Depression is a leading source of disability worldwide and significantly
contributes to the global burden of disease. According to the World Health
Organization (WHO)444https://www.who.int/health-topics/depression, depression
is a common psychiatric disorder characterized by a persistent undesirable
effect like sadness, lack of attention, or pleasure in formerly satisfying or
enjoyable activities. While the symptoms of the depression can be
psychological (e.g., feeling hopeless, having continuous sadness, feelings of
guilt and low self- esteem, loss of interest, having suicidal thoughts, and so
on), the link between the depression and physical symptoms (e.g., headache,
constipation, limb pain, stomach problems, pain at joints, back pain,
tiredness, appetite and weight changes, sleep changes, and so on) has also
been reported in the literature [62, 63]. The causes of depression comprise
complex relations among psychosomatic, social, and biotic factors. Life
moments such as childhood and teenage adversity, death, major events like
losing a job, genetics, and substance abuse may increase the chance of
depression. Pharmacological and psychosomatic treatments are available for
moderate and severe depressive disorder. However, in low- and middle-income
states, treatment and care facilities for depression are frequently absent or
undersized [64]. WHO3 reports that approximately 76–85% of people in such
countries have a lack of access to the depressive treatment they need.

According to Diagnostic and Statistical Manual of Mental Disorders (DSM-5) of
the American Psychiatric Association (APA) [65], depression exists in various
forms like Major Depressive Disorder (MDD), Disruptive Mood Dysregulation
Disorder (DMDD), Persistent Depressive Disorder (Dysthymia), Premenstrual
Dysphoric Disorder (PDD), Substance/Medication-Induced Depressive Disorder
(S/M-IDD), Depressive Disorder Due to Another Medical Condition (DDDAMC), and
Other Specified Depressive Disorder (OSDD) or Unspecified Depressive Disorder
(UDD).

MDD, a.k.a. clinical depression, is treated as the most typical form of the
disease, and it is diagnosed by the existence of at least four of the
following symptoms present for longer than two weeks [65, 66]: changes in
weight, changes in sleep, loss of energy almost every day, feelings of guilt
and worthlessness, psychomotor agitation nearly every day, difficulty in
concentrating, recurrent thoughts of death and suicide. In [67], the etiology
of MDD is associated with the genetic, biological, hormonal, immunological,
neurological, environmental factors, acute life events and
neuroendocrinological mechanisms. While other types of depression have common
symptoms with MDD, they mainly distinguish from MDD in a number of attributes.
For example, DMDD is non episodic and occurs in children and teenagers. It is
diagnosed by an obstinately ill-tempered, annoyed mood, and recurrent temper
bursts. Dysthymia is an incessant and chronic form of depression. PDD causes
severe irritability and nervousness in the week or two days before the period
starts [68]. Substance/Medication-Induced Depressive Disorder (S/M-IDD) is a
persistent form of MDD and occurs during or after substance intoxication.
Depressive Disorder Due to Another Medical Condition (DDDAMC) is caused by
long-term illnesses and ongoing pain in the body.

In the following subsections we will be focusing on diagnosis and assessment
of MDD, a.k.a clinical depression.

###  4.1 Assessment of Clinical Depression

Clinical Depression or MDD assessment includes an extensive checkup of the
patient,including examination of the mental state by discovering functional,
relational, societal issues, and psychiatric history. Previous incidences of
depression or mood rise,reaction to past treatment, and comorbid mental health
disorders are examined. Regarding the level and effect of functional loss, or
disability, clinical depression is assessed in mild, moderate or severe
levels. Based on this assessment, the DSM-IV (Diagnostic and Statistical
Manual of Mental Disorders) principles are used to make a diagnosis [69], and
the treatment is initiated. The benchmarks for diagnosing depression syndromes
and their clinical consequences are set by the Diagnostic and Statistical
Manual of Mental Disorders (DSM-5) [70]. In addition to a questionnaire-based
approach where verbal signs are considered in depression-assessment, another
followed approach is using biomarkers in which the biological process of the
human body that is ongoing or has happened used as depression indication.

####  4.1.1 Questionnaire-based assessment of Clinical Depression (Assessment
by Verbal Signs)

In this section, we highlight existing self-reported questioners-based
depression detection techniques and their limitations. These questioners are
designed for individuals and composed of questions to capture the signs of
depression such as desperation, irritability, feeling guilty, and physical
indications such as tiredness, weight loss, and lack of attention in sex, as
well. Many instruments have been used by clinicians for depression assessment.
Among these, we chose widely-accepted ones: PHQ-9, BDI, DSM-IV, CES-D, HAM-D.

The Patient Health Questionnaire (PHQ-9) is a multiple-choice self-report
inventory and used to monitor the severity of depression [71]. It consists of
nine items, each scored from 0 to 3, which results with a total score varying
from 0 to 27. Based on the score range where the total score of the
questioners fall into, subjects are categorized into different depression
levels like Minimal, Mild, Moderate, Moderate severe, and Severe depression.
Scoring system provided by PHQ-9 presented in Table 4. In [72, 60, 73], PHQ-9
is used for the participants’ selection for EEG based experiments.

Back Depression Inventory (BDI) [74], which is first published in 1961, is
another questionnaire-based approach commonly used as a valuation tool by
healthcare specialists and scientists to diagnose depression and anxiety. It
has three versions BDI, BDI-1A and BDI-II. The first BDI was published in 1961
with 21 multiple choice questions against four possible responses of the
patients. BDI-1A, which is a revised version of BDI, was published in 1970.
While BDI-1A provided ease of use, it still had some drawbacks, such as it
only address 6 criteria of DSM-III out of 9. At the latest version, BDI-II,
which was introduced in 1996, four items that are Body Image Change, Weight
Loss, Somatic Preoccupation, and Work Difficulty, are changed by Agitation,
Worthlessness, Concentration Difficulty, and Loss of Energy. Based on BDI-II,
depression is classified into Minimal, Mild, Moderate, and Severe depression
by the scoring system presented in Table 5. In [75, 56, 57], BDI is used as a
psychometric test, i.e., every question has at least four possible responses,
elongating in strength. A total high score shows the symptoms of severe
depression.

Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) [76], which is
published in 1994 by APA (American Psychiatric Association), has been used for
the diagnosis of It offers a consistent classification system for the
identification of mental health disorders for both adults and children. [55,
77, 78], used DSM-IV to monitor perinatal depression patients for an EEG
study. The updated version of DSM-IV was introduced in May 2013 with the title
DSM-5 instead of DSM-V due to the limitation of the roman numerals [65]. The
new version includes some essential changes from the previous one, such as
replacing the roman numbers to Arabic numbers, excluding Asperger’s disorder
and including disruptive mood dysregulation disorder.

The Center for Epidemiologic Studies Depression Scale (CES-D) was initially
designed for the general population. Since from last few years, it has been
used for the screening of depression patients in primary care centers [79].
The CES-D contains 20 self-report questions, scored on a 4-point scale, which
evaluate the type and level of depression experienced in the last few weeks.
The CES-D can be applied to people of all ages. It has been verified across
gender and cultural populations and achieve constant reliability and validity
[80].

The Hamilton Depression Rating Scale (HAM-D) [81] is a depression assessment
tool that consists of 17 items that are used for scoring. The HAM-D emphasis
on the wakefulness, desperateness, self-destructive thoughts, suicidal
thoughts, and actions. It is chiefly used to diagnose the depression recovery
in individuals before, during and after the treatment.

Questionnaire-based assessment approaches have several limitations. First,
they are prone to professional’s and patient’s subjectivity, which hinders the
objectivity of the process. Second, although depression presents different
symptoms [82] and has high co-morbidity, especially with anxiety [83], self
questionnaire-based approaches are unable to evaluate differences across
patient subgroups. Third, it is not capable of excluding participants already
diagnosed as having or being treated for depression. Forth, it frequently
performs a false diagnosis of bipolar disorder as a clinical depression [84].

Table 4: PHQ-9 scoring standard for depression detection ([85]). Depression Score | Depressive Severity
---|---
1-4 | Minimal Depression
5-9 | Mild Depression
10-14 | Moderate Depression
15-19 | Moderately Severe Depression
20-27 | Severe Depression
Table 5: Back Depression Inventory BDI-II Scoring Standard for depression detection ([86]). Depression Score | Depression Levels
---|---
0–13 | minimal depression
14–19 | mild depression
20-28 | moderate depression
29-63 | Severe Depression

####  4.1.2 Biomarkers-based assessment of Clinical Depression (Assessment by
Nonverbal Signs)

A biomarker is defined as “a characteristic that is objectively measured and
evaluated as an indication of normal biologic processes, pathogenic processes,
or pharmacological responses to a therapeutic intervention” [87]. The Major
Depressive Disorder (MDD) diagnosis and treatment can be enriched by
decreasing the useless treatment trials by having accurate predictive bio-
markers. Biomarkers-based assessment provides accurate predictions of
depression and bipolar disorder and is capable of adequately measuring the
changes in disease conditions.

The scientific community uses different invasive and non-invasive tools and
techniques as biomarkers to understand the mechanisms behind the MDD.

The Anterior insula metabolism, the Hippocampal volume, and the Subcallosal
cingulate cortex metabolism are neuroimaging-based biomarkers [88], and they
are used as Treatment Selection Biomarker (TSB) for major depressive disorder.
The limitation is that, these biomarkers may not be beneficial for long-term
depression treatment selection, and may fail in severe depressive conditions.
Literature works report that genetic factors contribute to developing
depression [89]. For example, it is shown in [90] that the gene 3p25-26 was
found in more than 800 families with recurrent depression. [91] presents a
review of EEG (Electroencephalograms) and ERP (Event-Related Potentials) based
predictive biomarkers for MDD. This study highlights 1) Alpha power and
asymmetry, 2) Theta band activations, 3) Antidepressant treatment response
(ATR) index, 4) Theta QEEG cordance, 5) Referenced EEG (rEEG), 6) Rostral
anterior cingulate cortex (rACC) activations and 7) machine learning as EEG
based predictive biomarkers 8) The P300 [92] and LDAEP [93] as ERP biomarkers.
Wake and sleep EEG is also used as a depression biomarker in [94] that provide
an overview of sleep variations in depression. It is reported in [95] that
60–90% of MDD patients with high severity suffers from sleep disorders.

In the past studies, while Alpha and Theta bands have been found to give
information about the depression diagnosis and recovery [96, 97], Gamma band
was not well recognized in depression diagnosis [98]. On the other hand, in
[99] Gamma waves are declared as depression diagnostic bio-marker by
presenting some significant findings on gamma pulses. In comparison to Alpha,
Beta and Theta waves, Gamma waves have some distinct attributes: 1) Gamma
pulses can accurately differentiate patients with major depressive disorder
from healthy controls, under certain disorders, 2) Gamma waves can
discriminate uni polar disorder from bipolar, 3) several pharmacological and
no pharmacological treatments that counter depression also affect gamma. [100]
adopts a variety of EEG features as depression diagnosis bio-markers extracted
by linear and nonlinear methods and Phase Lagging Index (PLI) at the resting
state of the patient.

[97] used the frontal Theta asymmetry as a depression biomarker. More
specifically, the EEG signals of 23 subjects with MDD and 23 are recorded
while they were listening to music. Results shows that frontal Theta power and
frontal Theta asymmetry increased significantly in healthy subjects and
decreased in depressed patients.

[101] proposed to use multi-modal bio-markers (combination of executive
dysfunctions, motor activity, neurophysiological patterns) for MDD diagnosis
since depressive disorder affects not only mood but also psychomotor and
cognitive functions. 20 MDD and 20 healthy subjects are selected. It is shown
that the multi modal bio-markers are more consistent in the identification of
MDD patients than the unimodal biomarkers are. [102] used brainwaves as a
potential bio-marker for risk analysis of MDD. These brain waves are not only
helpful in the depression diagnosis but it also provides the basic foundation
for the accurate and reliable treatment of depression.

The EEG-based depressive bio-markers have several advantages over neuroimaging
techniques [46]. While, the neuroimaging techniques are less effective and
unable to provide information about the treatment, EEG bio-markers are easy to
use, non-invasive, have high temporal resolution, cost-effective and provide
optimal treatment selection. Despite lots of advantages of EEG-based bio
markers, they have several drawbacks, such as poor measurement in below area
of cortex and poor signal to noise ratio that require large number of
participants for extracting useful information from EEG. The major bio-markers
that have been reported in the literature for depression diagnosis are shown
in Table 6.

Table 6:  Psychological Biomarkers for the assessment of depression. Ref |  EEG bio markers |  ERP Bio markers |  Neuroimaging Bio markers |  Genes and other Bio markers |  Common Findings
---|---|---|---|---|---
[96] |  Alpha power and Asymmetry(FAA) |  x |  x |  x |  FAA is able to distinguish MDD and healthy control based on gender, age and depression severity.
[97] |  Theta band activation’s |  x |  x |  x |  Frontal theta asymmetry increased in normal subjects but reserved in depressed patients during music listening.
[103] |  Antidepressant treatment response (ATR) index |  x |  x |  x |  ATR is a potential predictor and biomarker of NTR treatment of MDD pateints.
[98] |  Theta QEEG cordance |  x |  x |  x |  The change in QEEG distingusish MDD and normal control.
[104] |  Referenced EEG (rEEG) |  x |  x |  x |  It provides assistance in MDD medicine selection.
[105] |  Rostral anterior cingulate cortex (rACC) |  x |  x |  x |  Low rAcc is a best responder to depression treatment.
[94, 106] |  Wake and sleep EEG |  x |  x |  x |  The variation in wake and sleep helps in depression detection.
[99] |  Gamma waves |  x |  x |  x |  gamma can discriminate uni polar disorder from bipolar.
[107] |  Resting state EEG |  x |  x |  x |  Extracted EEG features helps in depression diagnosis.
[97] |  Frontal theta Asymmetry |  x |  x |  x |  It provides reliable classification in comparison to gamma.
[88] |  x |  x |  Anterior insula metabolism |  x |  It identify treatment outcomes of depressed patients.
[108] |  x |  x |  Psycho motor retardation |  x |  Affected by depression.
[88] |  x |  x |  Hippocampal volume |  x |  Depresses patient have 19% small hippocampal.
[109] |  x |  x |  Cognitive functions |  - |  Cognitive actives gets damaged by depression.
[20] |  x |  x |  Subcallosal cingulate cortex metabolism |  x |  Abnormal SCG distinguish the MDD and control subjects.
[110] |  x |  x |  x |  Vocal and Facial bio markers |  It is less reliable in comparison to EEG biomarker.
[90, 89] |  x |  x |  x |  3p25-26 |  This genes is found in more than 800 depressed families but still immature.
[92] |  x |  P300 |  x |  x |  It is used as indicator to measure the severity of depression.
[93] |  x |  LDAEP |  x |  x |  The higher LDAEP values found in depression patient.

##  5 Neural networks-based approaches for Depression recognition using EEG
signals

The fields of Affective Computing (AC) and Neural Networks (NNs) are useful in
solving complex and multidimensional problems such as modeling social
affective behavior and mental disorders. These problems involving affective
datasets need Deep Neural Networks (DNNs), neural networks with two or more
hidden layers, for effective temporal modeling, and real-time performance
analysis. The ability of DNN to identify latent structures in raw, unlabeled,
unstructured, noisy, and incomplete EEG datasets makes it suitable for EEG
based depression diagnosis. The effectiveness of DNN based solutions for
depression diagnosis depends on the EEG experimental protocols, placement, and
types of EEG electrodes, and the availability of EEG datasets[111]. This also
requires investigating automatic depression assessment methods.

###  5.1 EEG Experimental Protocols for Depression Recognition

The EEG experimental protocol for depression recognition defines a standard
set of rules such as number of participants, selection criteria of
participants, EEG recording duration,and so on. Different EEG-based
experimental protocols for depression recognition have been proposed in the
literature. This section discusses the EEG based experimental protocol for
depression recognition using shallow and deep neural networks.

Participants: The number of participants varies in different studies, from one
to two hundred, with a median of 30 subjects. Most of these studies are based
on a relatively small number of participants, therefore, it is hard to
establish the exactness and significance of the results. Table 7 presents a
summary of existing studies highlighting the number of subjects, gender, age
group, prior history, and depression. According to Table 7,only ten out of
fifty studies include thirty participants for the diagnosis of the EEG based
depression. Thirteen studies include 50 to 60 subjects with an equal number of
depressed and normal participants.The remaining studies consider different
numbers of subjects.Regarding gender selection, most of the studies include
both male and female participants; While, few studies include just female
participants for EEG based major depressive disorder detection. Since men and
women might perceive depression in different ways, so the participants from
both genders must be included in studies.

Table 7: Participants information in EEG experiments for depression detection where D for Depressed, H for healthy, F for female and M for male is used. Ref |  Subjects |  Gender |  Age Group
(Mean, ± SD) |  Other Information |  Depression Types
---|---|---|---|---|---
[53] |  63(33D+30H) |  27F,36M |  38-40 (38.22, 15.64) |  - |  Unipolar depression
[112] |  30(15D+15H) |  - |  20-50 |  - |  Bipolar depression
[75] |  51 |  15F, 36M |  18-24 (20.96, 1.95) |  - |  Mild depression
[54] |  30(15D+15H) |  - |  20-50 |  - |  Bipolar depression
[113] |  60(30D+30H) |  - |  20-50 |  - |  Bipolar depression
[114] |  10 |  - |  - |  - |  Depression
[115] |  60 |  30F, 30M |  10,32 (32.4, 10.5) |  - |  Minimal to severe depression
[55] |  28(14D+14H) |  - |  - |  Right handed |  Depression
[56] |  15 |  - |  - |  - |  Depression
[57] |  89 |  - |  - |  - |  Uni and Bipolar depression
[116] |  60(30D+30H) |  32F, 28M |  20-50 |  - |  Bipolar depression
[117] |  30 |  16F, 14M |  20-50 |  - |  Depression
[72] |  213(92D+121H) |  - |  - |  - |  Depression
[118] |  60(30D+30H) |  20-50 |  - |  - |  Depression
[119] |  16 |  - |  - |  - |  Depression Level
[120] |  51 |  15F, 36M |  18-24 |  Right handed |  Mild depression
[60] |  116 |  50F, 66M |  19-25 |  - |  Mild depression
[77] |  55 |  - |  - |  - |  Major depressive disorder
[73] |  34(17D+17H) |  17F, 17M |  30-33 (33.35, 12.36) |  - |  Depression
[121] |  22 |  - |  - |  - |  Mild Depression
[122] |  25(12D+13H) |  25F |  30-42 (24.23, 6.33) |  Right handed |  Pervasive depression
[123] |  178(86D+92H) |  - |  - |  - |  Depression
[78] |  24(12D+12H) |  10F, 14M |  20-28 |  - |  Major Depressive Disorder
[124] |  30(15D+15H) |  16F, 14M |  20-50 |  - |  Depression
[125] |  30D |  - |  20-50 |  - |  Depression
[126] |  64(34D+30H) |  26F, 38M |  15-38 |  - |  Major Depressive Disorder
[127] |  25(13D+12H) |  25F, 0M |  (24.23, 6.33) |  Right handed |  Major depressive disorder
[128] |  23(12D+11H) |  12F, 11M |  21-55 (26.4, 10.6) |  - |  Depression
[129] |  16 |  - |  - |  - |  Depression Level
[130] |  51(24D+27H) |  15F, 36M |  18-24 (20.96, 1.95) |  - |  Mild depression
[131] |  64(34D+30H) |  26F, 38M |  12-40 |  - |  Major depressive disorder
[132] |  12(12D) |  6F, 6M |  20-35 |  - |  Depression
[133] |  30(10D+10H+10S) |  - |  13-53 (31.5) |  - |  Depression
[134] |  22(12D+11H) |  10F, 12M |  20-24 |  - |  Depression
[135] |  20(10D+10H) |  10F, 10M |  - |  - |  Depression
[136] |  116(63D+53H) |  - |  19-25 |  - |  Depression
[137] |  63(33D+30H) |  27F, 36M |  38-40 |  - |  Unipolar depression
[138] |  22(12D+10H) |  - |  69-70 (69.81) |  - |  Depression

Selection Criteria of Participants: The selection of the participants is the
most important phase of EEG based depression recognition. The quantitative
(clinical questionnaire-based) and qualitative (EEG-based) methods are used as
standard tools for the selection of participants and data collection. In EEG
based mental disorder studies, several clinical questionnaire-based pre-
testing techniques are used in Table 8 for participants’ recruitment, and then
the patient’s selection for EEG recording.

Beck Depression Inventory (BDI) [74] is a common valuation tool used by
healthcare specialists and researchers for pre-testing of depression and
anxiety diagnosis. In this test, patients with MDD are selected based on
different multiple-choice questions. The patients with BDI-II score above 13
are considered as a depressive subject. Overall high score of BDI-II shows the
severity of depression. BDI is used in [53, 75, 115, 56, 57, 116, 117, 118,
120, 127, 129, 139, 130, 137] for participants selection. Diagnostic and
Statistical Manual of Mental Disorders (DSM-IV) developed by the American
Psychiatric Association (APA) has been used by several studies for the
measurement of different mental illnesses. In EEG based diagnosis, DSM-IV
based questionnaire is used for pre-psychometric test to assess depression.
Most of the articles used it as pre-EEG test [53, 55, 77, 126, 131]. Hospital
Anxiety and Depression Scale (HADS) is used in [53] to measure the different
levels of depression. Another participant selection method is known as PHQ-9
that is a multiple-choice self-report inventory. It consists of 9 questions
and is used as a selection/diagnostic tool for mental health syndromes such as
depression.

The subjects are categorized into different levels of depression such as
minimal, mild, moderate, moderately severe, and severe depression based on the
standard score of the questionnaire. The PHQ score standard is presented in
Table 4. The most of the articles use it for pre-screening of the subjects
[60, 73, 123, 136]. The participants are shortlisted for qualitative EEG based
recording after the pre-screening test, and approval of the design of study
from the ethics department. The participants perform specific tasks for a
particular duration in a calm room under a resting state with a different
number of non-invasive wet or dry electrodes at the different regions of the
scalp. The recorded EEG signals contain a lot of noise that is removed for
further processing of the signal and depression classification.

Table 8: Participants selection and experimentation tasks in EEG studies for depression detection. Ref |  EEG Selection Criteria |  Subject Inclusion criteria |  Tasks |  Depression criteria
---|---|---|---|---
[53] |  No psychotic disease |  DSM-IV |  BDI,HADS |  (BDI-II,HADS)score$>$14
[112] |  No medication |  Psychiatrists interview |  - |  -
[75] |  - |  Investigation |  Watch pictures |  BDI(II)score 14-28
[54] |  No medication |  Psychiatrists interview |  - |  -
[113] |  - |  Psychiatrists interview |  - |  -
[114] |  No mental disorder |  interview |  Working on ladder |  -
[115] |  No mental disorder |  BDI-II |  - |  BDI(II)score 14-28
[55] |  Free of neurological disease |  MINI interview, |  - |  (DSM-IV) Score
[56] |  No medication |  - |  Watch 5sec film of pictures |  BDI(II),
Hamilton score
[57] |  No mental disorder |  - |  Watch pictures |  BDI(II)score 14-28
[116] |  No mental disorder |  BDI-II |  - |  BDI(II)score 14-28
[117] |  No drug addiction |  BDI(II) |  Sit in rest position |  BDI(II)score 14-28
[72] |  No medication |  (PHQ-9), Mini-Mental State Exam(MMSE) |  Sound stimulation |  MMSE score
[118] |  No epileptic |  BDI(II) |  - |  score$>$14
[119] |  Free of drugs |  - |  - |  -
[120] |  No psychopathology |  BDI-II |  Watch picture |  BDI-score$>$14
[60] |  No psychopathology |  (PHQ)-9, DASS-21 |  DASS-21 |  DASS-score$>$14
[77] |  No mood stabilizer |  (DSM)IV,(HAM-D) |  Resting position |  HAM-D score$>$14
[73] |  Free of psychopathology |  (PHQ)-9 |  - |  (PHQ)-score$>$5
[121] |  No psychopathic history |  BDI-II |  - |  BDI-score$>$14
[122] |  Free of any medicine |  BDI(II) |  - |  BDI(II)$>$13
[117] |  No neurological history |  - |  - |  -
[123] |  Hospital inpatient |  PSQI,PHQ-9,GAD-7,MINI |  Play sound track |  PHQ-9$>$5
[78] |  No drug history |  DSM-IV,(BDI)II |  - |  (BDI)II-score$>$14
[124] |  No psychiatric drug |  - |  - |  -
[125] |  No mental disorder |  Written consent |  - |  -
[126] |  No mental disease |  (DSM)-IV,HUSM |  - |  (DSM)-score
[127] |  Free of any medications |  (BDI)II |  - |  (BDI)II-score$>$14
[128] |  Free of any medications |  Written consent |  - |  -
[129] |  Free of drugs |  (BDI)II |  - |  (BDI)II-score$>$14
[139] |  No psychopathology. |  (BDI)II |  Watch images |  (BDI)II-score$>$14
[130] |  No psychopathology |  BDI-II |  Watch pictures |  (BDI)II-score$>$14
[131] |  No mental disease |  DSM-IV |  - |  DSM-IV score
[132] |  No medication |  - |  Play the VR game |  -
[117] |  No mental disease |  Hospital patient |  - |  -
[133] |  No psychiatric disease |  Hospital patient |  - |  -
[134] |  No mental disease |  Hospital patient |  - |  -
[135] |  No mental disease |  Hospital patient |  - |  -
[136] |  No psychiatric disease |  PHQ-9, DASS-21 |  PHQ-9,DASS-21 |  PHQ-9$>$5
[137] |  No psychotic |  DSM-IV |  BDI,HADS |  (BDI-II, HADS) score$>$14
[138] |  No psychotic |  MOCA |  MMSE |  MOCA score

Placement and Types of EEG Electrodes:  The number of electrodes, their
placement, and type play an important role in the EEG based depression and
bipolar disorder diagnosis due to multiple reasons. First, the time required
to set up the EEG device, second, ease of the patient who wears the EEG
device, and third the number of features to process [140].For these reasons,
researchers have used different numbers of electrodes and standards to acquire
EEG signals from the scalp. According to Table 9, thirty one studies out of
fifty use wet electrodes to record the EEG signals and the remaining use dry
electrodes. The position of the electrodes at the scalp is also important
because different lobes of the cerebral cortex are responsible for processing
the electrical activities of the brain. As far as placement of electrodes at
the scalp is concerned, two standards of 10-20 and 10-10 exist in the
literature.

The 10-20 and 10-10 electrodes placement systems are based on an international
standard that describes the location of electrodes at the scalp. In 10-20
standard, "10" and "20" represent the 10% or 20% inter-electrode distance and
in 10-10 standard,"10" and "10" shows the 10% inter-electrode distance. These
standard electrode placement systems are based on the correlation between the
location of an electrode and the underlying area of the cerebral cortex. The
even numbers on the scalp represent the right hemisphere and odd numbers refer
to the left hemisphere. The letters F, T, C, P, and O stand for Frontal,
Temporal, Central, Parietal and Occipital respectively. They are used to
identify the brain lobes and place the electrodes at the scalp. The point z
refers to the midline of the brain. In the 10-20 electrode standard, the
smallest number is closer to the midline and vice versa. A bird’s eye view of
electrode placement is shown in figure 1(c). According to Table 9, twenty-five
studies adopt 10-20 standard, only two use 10-10 standard, and remaining do
not mention any information about electrode placement. According to Table 9,
number of electrodes varies in different studies. Researchers decide about the
number of electrodes according to their requirements and diagnostic criteria.

Table 9: EEG devices,number of channels and their placement used for EEG experiment for depression detection. Ref |  EEG Device |  Electrode |  Brain Lobes |  Placement /Standard |  Types of
Electrodes
---|---|---|---|---|---
[53] |  EEG cap |  19 |  - |  10-20 |  Wet
[112] |  Pair electrode |  2 Channel pair |  left/right half |  - |  Wet
[75] |  (HCGSN) |  128 |  - |  10-10 |  Dry
[54] |  (HCGSN) |  2 Channel pair |  Left/right half |  - |  Dry
[113] |  Bipolar Montage |  2 Channel pair |  Frontal lobe |  10-20 |  Wet
[114] |  Salivary cortisol |  14 |  - |  - |  Wet
[115] |  - |  19 |  - |  - |  Wet
[55] |  (HCGSN) |  16 |  - |  10-20 |  Dry
[56] |  Procomp |  1(F4) |  - |  - |  Dry
[57] |  Procomp |  1(F4) |  - |  - |  Dry
[116] |  - |  24 |  - |  10-20 |  Wet
[117] |  - |  2 Channel pair |  Left/right half |  - |  Wet
[72] |  - |  3(Fp1,Fp2,Fpz) |  Frontal lobe |  10-20 |  Wet
[118] |  Bipolar Montage |  2 Chanel pair |  Frontal lobe |  10-20 |  Wet
[119] |  Ag-Cl electrodes |  3 |  Frontal lobe |  10-20 |  Wet
[120] |  Geodesic,HCGSN |  16 |  - |  10-20 |  Dry
[60] |  32 Channel hardware |  16 |  - |  - |  Wet
[77] |  (Compumedics/ Neuroscan) |  6 |  Frontal lobe |  10-20 |  Wet
[73] |  HydroCel GSN (128) |  64 |  - |  10-20 |  Dry
[121] |  SynAmps |  61 |  - |  10-20 |  Wet
[122] |  Mobile EEG belt |  3(Fp1, Fp2 and Fpz) |  Frontal lobe |  10-20 |  Dry
[123] |  - |  3(FP1,FP2,FPz) |  Frontal lobe |  10-20 |  Wet
[78] |  - |  7(FP1-FP8) |  Frontal lobe |  10-20 |  Wet
[124] |  - |  2 Channel pair |  Left/right half |  10-20 |  Wet
[125] |  Bipolar Montage |  2 Channel pair |  Left/right half |  10-20 |  Wet
[126] |  ProComm |  1(F4) |  Frontal lobe |  - |  Wet
[127] |  EEG belt |  3 |  Frontal lobe |  10-20 |  Wet
[128] |  Brain amp |  6(Fp1, Fp2, F3, F4, P3 and P4) |  Prefrontal,parietal cortex |  10-20 |  Wet
[129] |  - |  3 |  Frontal lobe |  10-20 |  Wet
[139] |  Geodesic sensor net (HCGSN) |  128 |  - |  10-20 |  Dry
[130] |  HCGSN |  128 |  - |  10-10 |  Dry
[131] |  - |  19 |  Temporal,parietal |  10-20 |  Wet
[132] |  - |  3(Fp1, FpZ and Fp2) |  Prefrontal lobe |  10-20 |  Wet
[117] |  - |  2 Channel pair(FP1-T3)FP2-T4) |  left/right half |  10-20 |  Wet
[133] |  HCGSN |  16(Fp1-FP8, C3-C4, P3-P4, T3-T6, O1-O2) |  Frontal, ear lobe |  10-20 |  Wet
[134] |  - |  5(F4, F3, Cz, M1, M2) |  Frontal,earLobe |  10-20 |  Wet
[135] |  - |  64 |  - |  10-20 |  Wet
[136] |  32 channels wet EEG hardware |  16 |  place at whole scalp |  10-20 |  Wet
[137] |  EEG cap |  19 |  - |  10-20 |  Wet
[138] |  Nihon kohden JE-207A easy cap |  57 |  - |  - |  Wet

####  5.1.1 Public EEG Datasets for Depression Diagnosis

Due to the sensitive nature of depression data and for privacy and
confidentiality reasons, very few public datasets are available for EEG based
depression diagnosis, therefore, most research groups use their datasets. This
section presents the few publicly available datasets for EEG based depression
diagnoses as shown in Table 10.

Healthy Brain Network555Healthy Brain Network Dataset link
http://fcon_1000.projects.nitrc.org/indGi/cmi_healthy_brain_network/sharing_neuro.html:
HBN is a public biobank of data that is launched by the child mind institute.
The major goal of HBN is to produce a dataset that captures a wide range of
heterogeneity and impairment that occurs in evolving psychopathology. The HBN
contains information about depressive disorder, behavioral, intellectual, eye
tracking, and phenotype data, by using multi-modal EEG and brain imaging MRI.
In the HBN, a clinical assessment of mental health and learning disorders is
performed. The HBN protocols include the behavioral and physical measures,
family structure, stress, trauma, cognition, and language tasks. About 10,000
subjects of New York with ages between 5 and 21years participated in data
collection. The clinical team consisted of a mixture of psychologists and
social workers. The safety and eligibility of the participants was confirmed
by prescreening interview. The screening interview collected information about
the subject’s psychiatric and medicinal history.

EMBARC (Establishing Moderators/Mediators for a Biosignature of Antidepressant
Response in Clinical Care) is a public dataset made available by the National
Institute of Mental Health (NIMH). It recognizes the neurological sign of
reaction to antidepressant treatment by using the resting-state EEG and
machine learning algorithm. A total of 16 posterior electrodes were comprised
of P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, O1, O2, and Oz.
[141].

Depresjon public dataset is used in [142], it contains motor activity
recordings of 23 unipolar and bipolar depressed patients and 32 healthy
controls666Depresjon Dataset link https://datasets.simula.no/depresjon/#data-
collection.

Trans diagnostic cohorts [143] is a publicly available dataset that evaluates
the brief transdiagnostic cognitive-behavioral group therapy (TCBGT) for the
treatment of anxiety and depression patients. It contains 287 participants in
primary care with depression and anxiety disorders. These patients spent
approximately 5 weeks with TCBGT. ANOVA tests that have mixed design
capabilities have been used for statistical analysis and to check the effects
of treatment.

Multimodal Resource for Studying Information Processing in the Developing
Brain (MIPDB) focuses on neuro-phenotyping of psychiatric and healthy
populations of dimensional and multi-domains. They intellectualize mental
disorder in terms of domain-general discrepancies, instead of considering a
single factor[144].

Patient Repository for EEG data and computational tools (PREDICT) 777Patient
Repository for EEG data and computational tools (PREDICT) Dataset Link
PatientRepositoryforEEGdataandcomputationaltools(PREDICT is a high volume
publicly available dataset that contains EEG data. There exist several data
repositories that contain imaging and patient-specific data. Patient
Repository For EEG Data + Computational Tool (PRED + CT) is one of the few
sites that offer EEG based patient-specific data. It provides a centralized
platform by categorizing psychiatric and neural patients based on EEG data
storage, tasks, and computational tools. In PRED+CT, EEG data implementation
is performed in MATLAB toolbox based on patient/control, symptom scores, age,
and sex [145].

Table 10: Public EEG datasets for depression diagnosis. Ref |  Dataset Name |  Patient Group |  Task during EEG recording |  Number of Patients |  EEG system |  Electrodes
---|---|---|---|---|---|---
[146] |  Healthy Brain Network(HBN) |  Depression |  Resting state |  10,000 |  128-channel EEG geodesic hydrocel system |  128
[141] |  EMBARC |  Depression |  Resting state |  675 |  - |  16
[142] |  Depresjon |  Depression |  Motor activity |  55(23D+32H) |  - |  -
[143] |  Trans diagnostic cohorts |  - |  - |  287 |  - |  -
[147] |  PREDICT (Depression Rest) |  Depression /High BDI |  Resting state |  46 |  Neuroscan |  64
[144] |  MIPDB |  Depressive |  Resting state |  126 |  - |  109
[145] |  PREDICT (Depression RL) |  Depression RL/ High BDI |  Reinforcement learning |  46 |  Neuroscan |  64

###  5.2 Investigating Automatic Depression Assessment Methods

In this section, automatic depression assessment methods are investigated by
different prepossessing techniques, Neural networks (NNs), and deep learning-
based approaches for depression detection.

####  5.2.1 Pre-processing

The EEG signal recording is a time-consuming procedure in which depressed
patients perform some tasks. During recording, EEG signals are contaminated by
undesired or polluted signals called artifacts. The artifacts that occur due
to the patient body movement, heartbeat, eye blinks, muscle movement are
called physiological artifacts. The artifacts that occur due to the electrodes
placement, environmental noise, and device error are known as non-
physiological artifacts [148]. These artifacts affect the quality of the
actual EEG signals; hence it is important to sanitize useful data from
contaminated EEG signals through pre processing of the EEG data.

In pre processing phase , different undesired artifacts are filtered by using
different noise removal and artifact elimination algorithms to prepare data
for the next stage. Table 11 shows the different artifact removal techniques.
Mumtaz et al. [53], claim that raw EEG signals have poor resolution due to the
low Signal-to-Noise ratio (SNR). Therefore, to enhance the performance of EEG
signals, the multiple source eye correction (MSEC) technique is used to remove
the undesired signals.

The authors of references [112, 54, 118, 117, 124, 125, 132, 136] used a Notch
filter with 50Hz to remove power line intervention and to sanitize EEG signals
from artifacts. While, articles [75, 114, 115, 122, 124, 130, 131, 135, 136]
used a low pass and high-pass-filter with 40Hz and 1Hz cutoff frequency to
filter the noise. Adaptive noise canceller and fast ICA is used in [55, 138]
to remove inaccurate information from the false EEG recordings.

As per our findings, the low pass, high pass, and Notch filter are frequently
used to remove the artifacts in EEG based depression detection.

Table 11: Artifacts and noise filtering approaches in EEG based depression detection.  Ref |  Artifact filtering techniques
---|---
[53] |  MSEC
[112] |  Notch filter:50Hz
[75] |  High pass, net station waveform
[54] |  Notch filter:50HZ
[113] |  -
[114] |  Low-pass, high-pass, notch filter
[115] |  Highpass, butterworth,low-pass
[55] |  Adaptive noise canceller, Hzband-pass, fastICA
[56] |  -
[57] |  Band pass filter 40HZ
[116] |  Total variation Filtering (TVP)
[117] |  Visual inspection, total variation filtering (TVF)
[72] |  Kalman filter, adaptive predictor filter
[118] |  Notch filter:50Hz
[119] |  -
[120] |  Net station waveform tool
[60] |  High pass, Low pass, and Notch filter
[77] |  Band pass filter (0.15–30Hz)
[73] |  FastICA, hanning filter
[121] |  Off line ICA
[122] |  Low pass filter at 50HZ
[117] |  Notch filter at 50HZ
[123] |  Band pass filter
[78] |  Wavelet filter
[124] |  Notch and low pass filter at 50HZ
[125] |  Notch filter at 50HZ
[128] |  Convolutional filter
[129] |  ANFIS
[139] |  Net station waveform tools
[130] |  Low pass,high pass filter
[131] |  ICA, notch, low pass,high pass filter
[132] |  Notch filter
[117] |  Visual inspection, 50-Hz notch filter
[133] |  EEGLAB toolbox
[134] |  Stationary wavelet transform (SWT)
[135] |  Lowpass and highpass filter
[136] |  Lowpass, highpass and notch filter
[137] |  MSEC
[138] |  ICA and butterworth filter

####  5.2.2 Neural networks-based approaches for depression recognition

Neural Networks (NNs) [149] are non-parametric, flexible, and parallel
computing systems that consist of neurons layers and weighted links in which
information is transferred from the input neurons to the output neurons in a
forward or backward way. Neural Networks are broadly classified in to feed
forward and feed backward network.

A feed forward neural network (FFNN) is developed by structured layers that
are analogous to human brain neurons. Each layer consists of several connected
units. The connection of units and layers are not uniform because each
connection have different strength and weights. Example of FFNNs is the
multilayer perceptron (MLP)[60].

In recent years, ANN-based approaches are used in EEG studies for the
classification and diagnosis of major depressive disorder. ANN can classify
nonlinear relations and incorporates high-order dealings between predictive
variables to produce accurate results. Most of the articles [57, 77] use it
for the classification of unipolar and bipolar depression and have achieved
good accuracy of 89.09% by implementing the pre-treatment cordance of frontal
QEEG.

Multi-layer FFNN, Back Propagation Neural Network (BPNN), and Enhanced
probabilistic neural network (EPNN) have been used in [60, 122, 78] for
discriminating MDD and non-MDD patients. In the test, EEG recording of
depressed and age control subjects were collected under the 10 cross-
validation technique. EEG electrodes were placed at the prefrontal (Fp1 and
Fp2), frontal (F3, F4, F7, and F8), cerebral (C3 and C4), and temporal (T3,
T4, T5 and T6) regions of the brain. The results indicate that the signals
collected from the cerebral (C3 and C4) region gives slightly higher accuracy
as compared to the other brain regions. Multi-layer FFNN performs better than
BPNN and EPNN by achieving 95% classification accuracy.

In [56, 116, 117], a comparative study of FFNN, neuro-fuzzy networks, relative
wavelet energy (RWE), and probabilistic neural networks (PNN) has been
performed for differentiating the depressed and normal patients through EEG
signals. Classification abilities of neural, neuro-fuzzy network, and relative
wavelet energy (RWE) networks are authenticated by the EEG recordings. The
FFNN leads the PNN, neuro-fuzzy networks, and relative wavelet energy (RWE)
achieving 100% classification accuracy with time and wavelet energy as the
input features.

EEG entropies based depression detection has been performed in [124, 150] by
comparing the Probabilistic Neural Network (PNN) performance with Radial Basis
Function (RBF) and different machine learning classifiers. Results shows that
PNN performs better with 99.5% classification accuracy.

####  5.2.3 Deep learning based approaches for depression detection

Deep learning is the most popular area of research and currently, it is
extensively used for the classification of EEG signals in comparison to other
approaches[151]. Deep learning gives accurate predictions,in EEG based
depression classification, than traditional machine learning due to the huge
volume of data for training. In comparison to the traditional Artificial
Neural Network (ANN), that are trained by a supervised algorithm with limited
to few layers, deep learning uses multiple layers to extract complex features
from the raw EEG data [152].

The convolutional neural network (CNN), long Short-Term Memory (LSTM),
recurrent neural network (RNN), denoising autoencoder (DAE) and deep belief
networks (DBNs) are the popular deep learning methods that are widely used in
most of the EEG studies to diagnose major depressive disorder[153]. The
performance comparison of all these methods in existing EEG based depression
detection studies are shows in Table 12.

A literature survey shows that approximately there are six different areas of
EEG: sleep, seizure prediction, mental workload, motor imagery, emotion
recognition and depression that use deep learning for the detection and
classification purposes [154]. According to the latest survey on deep learning
[154], it is observed that there are 16% articles for EEG based emotion
recognition system, 22% for motor imagery, 16% for mental workload, 14% for
seizure detection, 9% for sleep stage scoring, 10% of event-related potential
detection, 2% for EEG based depression and 8% for Alzheimer’s, gender and
abnormal signal classification(Please see Fig. 2).

Herein, we see that the motor imagery is the most frequently explored area but
the use of deep learning in EEG based depression recognition is not much
reported so far. In [53] authors claim that there are several articles on the
automatic diagnosis of depression with traditional classifiers, however, there
are only two or three articles on deep learning methods for EEG based
depression detection.

Figure 2: Ratio of Deep Learning studies adopting EEG signals for a variety of
applications referred to [154].

In the existing studies, questionnaires [37], visual cues [5], regression and
machine learning-based techniques [17] are used for the depression recognition
and treatment. Besides all these efforts, the depression detection field still
needs improvements. To enhance the feature extraction and classification
accuracy for depression recognition and assessment tasks, deep learning is
used for the last two years.

The Convolutional Neural Network (CNN) is the most famous model of deep
learning and is widely used in most of EEG studies to diagnose major
depressive disorder. One Dimensional Convolutional Neural Network (1DCNN) is
used with Long Short Term Memory (LSTM) for the classification of unipolar
depression [53]. This hybrid architecture achieves 95.97% accuracy by
automatically learning EEG patterns. The cerebral cortex is divided into the
left and right hemispheres of the brain. In [112], the authors used Deep
Convolutional Neural Networks to classify depressive and non-depressive
subjects against the left and right hemispheres. Results shows that the right
hemisphere is more accurate in depression classification as compared to the
left hemisphere with 93.5% accuracy. Mild depression recognition of college
students has been performed [75] by the convnets framework.

The convnets framework achieves 85.62% accuracy with 24-fold cross-validation.
Automatic depression detection using deep representation and sequence learning
is performed in [54]. These two hybrid architectures of deep learning provide
sequence learning and extract the temporal properties of the EEG signals. It
achieves 97.66% classification accuracy by placing the electrodes at the left
and the right hemisphere of the brain.

The Neucube architecture of deep learning with 10 cross-validations is applied
in [121] with the resting state of EEG. The neucube architecture is compared
with Multi-layer Perceptron (MLP) and other traditional machine learning
methods. The results shows that neucube performs better than MLP and all other
traditional machine learning algorithms such as SVM, decision tree and
logistic regression. Deep learning-based depression detection with frontal
imagery EEG channels is performed in [137]. The Oxford Net (VGG16) model with
$3\times 3$ max pooling and softmax activation function is used. The results
shows that OxfordNet (VGG16) has 87.5% classification accuracy. Functional
connectivity and EEG based mild depression detection has been performed in
[155] by using a deep learning approach. Initially, abnormal functional
connectivity is measured by using the graph theory, and then CNN is applied
for the classification of the depression with 80.74% accuracy.

Deep neural network approaches have also been used for feature extraction for
transformation of the huge volume of input data into imperative features sets.
In Major Depressive Disorder (MDD), the feature extraction phase selects the
most imperative features or information from the EEG signals for depression
classification. The feature extraction part has a great influence on the
accuracy of the results.

In [53] temporal feature is extracted efficiently by using the One Dimensional
Convolutional Network (1DCNN). The first layer of the CNN layer maps the 64
features, the next layer reduced it from 64 to 48, and the third layer
includes 24 features only by using the pooling layer and gets better
experimental results. In [112] authors do not use any manual set of features
to be fed into a depression classifier. The given CNN model has the capability
to self-learn and select the unique features during the training process
without a separate feature selection step.

In [75] pre-trained model based on transfer learning is used to extract the
power spectrum of EEG signals observing the differences between depressed and
non-depressed patients in alpha, beta, and theta frequency bands. The pre-
trained model removes the last output layer of CNN and then uses the entire
network as a fixed feature extractor. In [54] both local and long term feature
selection/extraction and depression classification operations are
automatically performed by using an end-to-end single framework and EEG
signals as input. Convolutional and pooling layers of deep learning models are
used in [114] for feature extraction. The benefit of these layers is that they
enhance the classification accuracy by extracting the features from the
nearest neighboring pixels. Brain has a nonlinear and complex system so
nonlinear features including fuzzy entropy FuzzyEn and fractal dimensions (KFD
and FFD) are used in [115].

Ensemble learning and deep learning approaches are applied in [55] for the
processing of features from brain signals, power spectral density and activity
are extracted as original features. Neucube model as a feature extractor is
applied in [121]. It achieves not only high classification accuracy, but also
reveals patterns of brain activities relevant to the two classes of depressive
and non-depressive subjects.

The performances of a variety of EEG based deep learning and neural network
approaches for depression detection are presented in Table 12.

Table 12: Performance of different EEG based deep learning and neural network approaches for depression detection presentation in terms of accuracy.  Ref |  Model used |  Results(Accuracy)
---|---|---
[53] |  1DCNN+LSTM based on repeated blocks of (C1-P1-DP1) layers |  98.32%
[112] |  13 Layer of Convolutional neural network |  95.49%
[75] |  Computer-aided design using convNet |  85.62%
[54] |  Hybrid model (CNN+LSTM) |  99.12%
[113] |  3 layer Convolutional neural network |  99.31%
[114] |  CNN and Fully Connected Neural Network |  86.62%
[115] |  Fuzzy neural network (FFNN) |  87.5%
[55] |  7 layer CNN and SVM |  84.75%
[56] |  3 layer NN and Neuro-Fuzzy network |  13.97% and76.88%
[57] |  FFNN-Particle Swarm Optimization |  89.89%
[116] |  Feedforward, probabilistic neural network |  58.75% and 98.75%
[117] |  Relative wavelet energy (RWE) and artificial feedForward neural network |  98.11%
[72] |  SVM, KNN, CT, ANN |  72.56%
[118] |  5 layer CNN |  99.3%
[119] |  Neural network pattern recognition tool (nprtool) and ANFIS tool box |  91.7%
[120] |  Self Normalizing Neural Networks (SNN) |  83.42%
[60] |  3 distinct layer feed-forward neural network |  95%
[77] |  Artificial Neural Network with 5 neurons |  89.09%
[73] |  4-layer ConvNet |  77.20%
[121] |  SNN NeuCube architecture. |  90%
[122] |  Back-Propagation Neural Networks (BPNN) |  94.2%
[117] |  Feed forward neural network |  98.11%
[123] |  Artificial Neural Network, Deep Belief Networks |  78.24%
[78] |  Enhanced probabilistic neural network |  91.3 %
[124] |  Probabilistic neural network |  99.5%
[150] |  Artificial neural network with 20 neurons |  -
[156] |  Multilayer perceptron |  80%
[125] |  Recurrent neural network,Long short term memory |  80%
[126] |  Multiple-layer perceptron neural network |  93.33%
[127] |  Back-Propagation Neural Networks |  94.2%
[128] |  Hybrid EEGNet |  79.08%
[129] |  ANFIS, NPR (neural network tool) |  91.7%
[139] |  ANN (BNMLP) |  83.42%
[130] |  Artificial neural network |  85.62%
[131] |  Multilayer perceptron neural network |  93.33%
[132] |  FBNN |  70%
[117] |  Artificial neural network with 10 neurons |  98.11%
[133] |  Artificial neural network with one hidden layer |  -
[134] |  Back-Propagation Neural Networks |  -
[135] |  Artificial neural network |  -
[136] |  Artificial neural network |  95%
[157] |  Probabilistic neural network |  98%
[158] |  Deep learning (SPN) |  -
[137] |  Multilayer Convulational neural network |  87.5%
[138] |  Multilayer perceptron (MLP) |  95.45%
[159] |  Stochastic gradient descent algorithm with deep neural network |  95.45%
[155] |  Two stacked convolutional neural networks |  80.74%

##  6 Clinical background of Bipolar Disorder

Bipolar Disorder (BD) or manic-depressive illness (MDI) is a dangerous neural
disorder that is predicted by mood uncertainty and may start from early ages
(i.e., in infants and teenagers). Individuals with BD face swings between
depressive and manic episodes recurrently [15]. According to the American
Psychiatric Association [16], there are four major categories of bipolar
disorder: bipolar I disorder, that has one atleast one full episode of mania
or diverse episodes of mania and depression. Bipolar II disorder has no manic
episode, minimum one hypomanic episode and many depressive episodes. Patients
with cyclothymic disorder have many hypomanic and depressive episodes. Bipolar
disorder includes both depressive and hypomanic episodes alternatively.

Bipolar disorder and depression have various resemblances, yet they have some
crucial dissimilarities [160]. Patients with bipolar disorder have experience
of high mood swings, different episodes of depression and periods of excessive
highs (also known as mania). In contrast, depression is just one state of
bipolar disorder that is more than from the low feeling. It’s a deep grief or
hollowness that an individual cannot manage. Sometimes, bipolar disorder
patients feel disheartened, valueless, tired and may fail to concentrate on
activities that they had enjoyed before the illness. Clinical depression (also
known as major depressive disorder or MDD) frequently comes with sleep
difficulties, and alterations in hunger. It can lead to suicidal activities or
actions. People with major depressive disorder do not practice any extreme,
elevated feelings that bipolar patients patients face with mania or hypomania.

In bipolar disorder, the symptoms of intense periods with low and high moods
do not follow a pattern. Some bipolar patients experience the same mood state
for a long period before shifting to the opposite mood and vise versa [161].
These depressive periods occur for a week, month, or sometimes even for a
year. Bipolar severity varies with respect to person and occurrence time.
Bipolar disorder symptoms are broadly classified into two types: 1) External
and physical, and ii) Internal and physiological symptoms.

Bipolar disorder has a strong physical effect on patient’s body and creates
many physical problems of flu, palpitations, diarrhea, abdominal pain, nausea
and vomiting, high pulse and heart rates, higher blood pressure, weight and
appetite changes, fast speaking and poor attention, strangely high sex drive,
enhanced energy and less need for sleep [162, 163]. The primary physiological
symptoms of bipolar disorder are penetrating and unpredictable. It includes
severe mood swings, extreme pleasure, hopefulness and excitement, rapid
changes from being glad to being ill-tempered, irritated, aggressive and
becoming more thoughtless. In the following subsections we will focus on
diagnosis and assessments of clinical bipolar disorder.

Irregular physical features of brain or an inequality in certain brain
chemicals are the main causes of bipolar disorder. The assessment of bipolar
disorder is not always easy like other mental disorders [164]. A bipolar
patient most probably goes to the consultant for the first time when he/she
has a depressive episode instead of during a manic or hypomanic episode. Due
to this reason, in the beginning clinicians frequently misdiagnose bipolar
disorder as depression. By considering all limitations of bipolar disorder,
scientists and researchers have introduced some automatic and reliable sources
for the diagnosis and treatment of bipolar disorder. Nothing is more
significant than diagnosing a patient with bipolar disorder or manic-
depressive illness (MDI), as only accurate diagnosis can lead to a proper
effective treatment. Electroencephalogram (EEG) is a top rated neuroimaging
technique that is becoming a central focus of researchers for the past few
years and is widely used to diagnose mental disorders.

###  6.1 Questionnaire-based assessment of Bipolar disorder (Assessment by
Verbal Signs)

Like major depressive disorders, questionnaire based assessment tools are also
used for bipolar disorder participants to investigate their physiological
responses. The Young Mania Rating Scale (YMRS), Hamilton Depression Rating
Scale (HDRS) and Structured Clinical Interview for Diagnostic (SCID), are the
few bipolar assessment tools that are widely used for participants selection
[57]. The YMRS [165] is one of the most widely used rating scale to evaluate
the mania symptoms. It consists of 11 items to evaluate the clinical condition
of patients in the last 48 hours. The purpose of each item is to measure the
abnormality of the patient. After this, HDRS is used to measure the severity
of the disease. Based on the clinical feature and symptoms, patients are
categorized into different diseases e.g., YMRS=12 show bipolar disorder,
YMRS=3 shows depression and YMRS=2 shows ethymia.

The HDRS [166] was originally published in 1960 and was used to evaluate the
recovery process (and also to measure the severity) of major depressive and
bipolar disorders. It contains ten multiple choice based questions that
provide an indication to bipolar depression. It usually works for adults and
measure the severity level of depression and mania according to the HDRS total
score. The SCID [167] is a structured diagnostic tool developed in 1990 and it
works with different versions of DSM to determine their axis. It is organized
into different modules and each module is used to detect unique type of
disorder i.e., SCID-I diagnose mental disorders, SCID-II determine personality
disorders and SCID-5 diagnose the anxiety, eating, gambling and sleep
disorders.

###  6.2 Bio markers based assessment of Bipolar disorder (Assessment by
Nonverbal Signs)

The pathophysiology of bipolar disorder is complex, multi factorial, and not
fully understandable. To overcome this complexity, the biomarkers based
assessment not only facilitates the diagnosis and monitoring of complex
bipolar disorder, but also provide biological effects of treatment. These
assessments devise a new hypotheses about the causes and pathophysiology of
bipolar disorder. The peripheral biomarkers, neurotrophins, oxidative stress
and neuroinflammation are used to measure the illness activity of bipolar
disorder[168]. The neurotrophins [169] is a family of proteins that induce the
survival, development, and function of neurons. It shows distinct patterns in
different stages of bipolar disorder, authors in [170], use it as brain-
derived neurotrophic factor (BDNF) bipolar disorder biomarker. The oxidative
stress is an imbalance of antioxidants and free radicals in the human body.
Its ratio increases during bipolar disorder episodes therefore in [171]
oxidative stress is used to diagnose the bipolar disorder. Neuro inflammation
is a localized physical condition of the body that becomes reddened or swollen
during bipolar disorder. The authors of [172] use it for bipolar disorder
study. The spectral entropy modulation that quantifies the EEG signal degree
of uncertainty gets less during manic episodes of bipolar disorder, so in [9]
it is used as a bipolar disorder and schizophrenia biomarker for altered
function. The brain oscillations and lithium response variate during bipolar
disorder condition and is used as a bipolar disorder biomarker in [173] .

##  7 Neural Networks based approaches for Bipolar disorder recognition using
EEG signals

Bipolar disorder (BD) is a composite disorder that oscillates between two mood
states of depression and mania and is often misdiagnosed by physicians [15].
It is an enduring condition in which BD patients spend most of their life in a
misery of symptoms of depression, which complicates accurate identification
and analysis of bipolar disorder. The fundamental clinical challenge of this
disease is the differentiation between BD patients and the patients showing
symptoms of general depression. Neural networks (NNs) based approaches offer
new ways to predict bipolar disorder recognition and clinical outcomes for
individuals, they reduce the complexity of bipolar disorder identification
process. NNs process the psychiatric data that is relayed on the brain
structure and resolve the complex real-world problems which is otherwise
difficult for conventional tools and techniques. In contrast to the human data
processing ability, NNs provide better precision and time effective solutions
for pattern recognition and prediction problems.

Multi-layer perceptron (MLP) is the most popular supervised neural network
that is widely used for classification and assessments. It is a class of FFNNs
(Feed Forward Neural Networks) that contains at least three layers: an input
layer, a hidden layer and an output layer. In [174], MLP is used as BD
(Bipolar Disorder) classifier with five neurons and one hidden layer. To
acquire the best conceivable precision, several discriminative features can be
extracted from the EEG recordings by using the four diverse feature selection
algorithms. These algorithms are CMIM (Conditional Mutual Information
Maximization), MIM (Mutual Information Maximization),DISR(Double Input
Symmetrical Relevance) and FCBF(Fast Correlation Based Filter) . The extracted
features can be fed to the MLP for classification purpose. Results shows that
MLP achieve 91.83% classification accuracy for bipolar disorder sub types and
normal subjects [174]. The artificial neural networks and quantitative EEG
have been used in [175] for differentiating fronto temporal dementia from
late-onset bipolar disorder. All patients are assessed by the clinical MRI
scan(Magnetic Resonance Imaging) and EEG(Electroencephalogram). The results
show that a combination of EEG and MRI with ANN classifier gives better
classification output as compared to EEG and MRI separately.

As one of the major unbearable neural syndrome, bipolar disorder is commonly
misdiagnosed as UD(unipolar disorder) , that further leads to suboptimal cure
and poor results. Therefore,the classification of UD and BD at initial phases
can therefore support to assist effective and precise treatment. In [57]
artificial neural network classifier with quantitative EEG is used as a
biomarker for the classification of the unipolar and bipolar disorder and it
achieves an accuracy of 89.89%. A bipolar depression patient experiences both
manic and depressive periods. To classify these two depressive periods, the
feedforward neural network (FFNN) and probabilistic neural network (PNN) is
used in [116]. The FFNN showed 98.75% classification accuracy while PNN
achieved an accuracy of only 46.5%. The results show that FFNNs give better
classification results for bipolar disorder as compared to PNN.

Classification of bipolar EEG signals in normal and depressive condition has
been performed in [117] by using relative wavelet energy (RWE) and an
artificial feed forward neural network. The performance of the artificial
neural network has been assessed by the classification accuracy and its value
of 98.11% shows its unlimited potential for classifying normal and depressive
subjects. In [176] convolutional neural network with electroencephalography
features is used for the precise diagnosis of the depression sub types. It
achieves 99.5% accuracy in the classification of unipolar vs healthy subjects
and 85% in discrimination of bipolar vs healthy subjects. In [177], bipolar
and schizophrenia disorder diagnosis is achieved by using an artificial neural
network. It achieves 90% classification accuracy among the bipolar,
schizophrenia and healthy subjects.

###  7.1 EEG Experimental Protocols for Bipolar disorder Recognition

EEG experimental protocols are a set of rules that are defined before EEG
recording. It includes the number of subjects that have participated in the
EEG based study, selection criteria of participants, placement standard and
types of electrodes that are used for recording the bipolar activity.

Participants: The ratio of the number of participants for the diagnosis of
bipolar disorder varies in different studies according to their resources and
requirements. Participants’ strength, gender, age group, prior history of
medication has a great effect on the diagnosis of bipolar disorder; therefore,
most of the studies consider it as their initial protocols requirements as
shown in Table 13. As far as participant strength is concerned, [174, 175]
include 38 subjects with the age group in the range of 15 to 16 years and
18:20 ratio, which means 18 subjects belongs to the bipolar disorder type I
and 20 belongs to the bipolar disorder type II. No distinction about gender is
mentioned in this study. To classify the unipolar, bipolar and healthy
subjects [57, 116, 117, 178] have performed EEG experiments for 30 ,60, 89 and
134 subjects in which half of the participants are depressed and the others
are healthy. Participants age varies from 20 to 50 years with no
discrimination of gender. The classification of bipolar, schizophrenia and
healthy subjects is performed in [177] in which 35 participants have
schizophrenia, 35 are with bipolar illness and the remaining 35 are healthy
subjects. From the available literature of bipolar disorder diagnosis, it is
observed that there is no standard ratio of number of participants and it
varies in different studies according to their resources and requirements of
the experiment.

Table 13: Participants information in EEG experiments for bipolar disorder detection. Ref |  Subjects |  Gender |  Age Group
(Mean,±SD) |  Bipolarity
---|---|---|---|---
[174] |  38(18BDI,20BDII) |  18F, 20M |  15-16 (15.7,1.5) |  Bipolar disorder I, II
[175] |  38(18 bvFTD+20BD) |  - |  52-77(64 years) |  Bipolar disorder
[57] |  89 |  - |  - |  Unipolar, bipolar
[116] |  60(30D+30H) |  32F, 28M |  20-50 |  Bipolar depression
[117] |  30 |  16F, 14M |  20-50 |  Bipolar depression
[178] |  134 (75D+59H) |  - |  18-54(35,4.2) |  Bipolar disorder
[177] |  105 (35SZ+35BD+35H) |  - |  - |  Bipolar disorder

Selection Criteria of Bipolar Disorder Participants: The participants are
selected based on two major parameters of self-reported psychometric test and
inclusion criteria of participants. For all subjects, the inclusion criteria
are history of epilepsy, head injury, psychiatric disorders and effect of
illegal drugs as shown in Table 14. The Diagnostic and Statistical Manual of
Mental Disorders (DSM-IV) and Back Depression Inventory (BDI-II) are the two
major self-reported psychometric tests that are used in bipolar studies [116,
117, 178, 177] for primary selection of the participants. Based on inclusion
criteria and psychometric test, only those participants are considered for EEG
study that have no psychotic disorder and their self-reported psychometric
test score is above 14. Interview by a psychiatrist is also conducted in some
studies [112] to ensure that the selected participants are taking no
medication to treat bipolarity and depression. After the selection of the
participants, a ten minutes EEG experiment is performed with open and close
eye conditions for each subject.

Table 14: Participant selection and experimentation tasks in EEG studies for bipolar disorder detection. Ref |  EEG Selection Criteria |  Subject Inclusion criteria |  Tasks |  Bipolar Disorder criteria
---|---|---|---|---
[174] |  No psychotic |  DSM-IV,BDI |  - |  (BDI-II score > 14
[175] |  Brain hospital patient |  DSM-5 |  - |  DSM-5 score
[57] |  No mental disorder |  |  Watch Pics |  BDI(II)score 14-28
[116] |  No mental disorder |  BDI-II |  - |  BDI(II)score 14-28
[117] |  No neurological history |  - |  - |
[178] |  Heroin addiction aatients |  DSM-IV |  Stay relaxed and awake |  DSM-IV score
[177] |  Age no more than 65 |  DSM-IV |  - |  -

Placement and Types of EEG Electrodes: The electrode placement and types of
electrodes play a major role in EEG based bipolar disorder data acquisition.
Minor mistakes in electrode placement pollute the overall EEG results that
further affect the classification of the EEG signals. The two major
international electrode placement standards at the scalp are 10-10 and 10-20
[179]. According to the literature presented in Table 15 most of the studies
used international placement standards [174, 175, 116] but few create their
own electrode placement strategy [57, 117]. Wet and dry types of electrodes
are used in EEG based studies to diagnose bipolar disorder. Wet electrodes are
usually made of silver and silver chloride material and applied on a scalp by
using the electrolytic gel material that works as a conductor between the skin
and the wet electrodes. Dry electrodes consist of a single metal and can be
directly placed on the scalp without the need to apply the conductive gel. Dry
electrodes are the most efficient and easy to use as compared to wet
electrodes. As patients feel comfortable with dry electrodes therefore latest
technologies prefer to use them [180].

Table 15: EEG devices,number of channels and their placement used for EEG experiment for bipolar disorder detection. Ref |  EEG Device |  Electrode |  Brain Lobes |  Placement Standard |  Types of
Electrode
---|---|---|---|---|---
[174] |  EEG cap |  19 |  - |  10-20 |  Wet
[175] |  |  19 |  Frontal and temporal |  10-20 |  Wet
[57] |  Procomp |  1(F4) |  - |  - |  Dry
[116] |  - |  24 |  - |  10-20 |  Wet
[117] |  - |  Two channel pair |  Left/right half |  - |  Wet
[178] |  “Neuroscan/ scan LT” neuro-headset |  - |  - |  - |  -
[177] |  - |  - |  - |  - |  -

####  7.1.1 EEG based public datasets for bipolar disorder.

Several public datasets, such as BioGPS [181], Bipolar Disorder Neuroimaging
Database [182], Bipolar Disorder Phenome Database [183] etc., exist for
bipolar disorder recognition. However, after an extensive literature survey,
we found that no EEG based public datasets are available for bipolar disorder
recognition research.

###  7.2 Automatic assessment of Bipolar disorder

Automation of bipolar disorder assessment is a major challenge for the
research community. The automatic methods make the investigation procedure
quick and easy. In these methods, features are automatically extracted from
the EEG signals and then based on these features a mental disorder is
diagnosed. In this article, artificial neural network and deep learning-based
approaches are considered for automated bipolar disorders diagnosis.

####  7.2.1 Pre-processing

The pre-processing or artifact removal is a major part of EEG data
acquisition, except this activity no analysis can be performed directly on the
EEG data. It is necessary to filter the EEG signals from the different
physiological and non-physiological artifacts and interference before using
it. The researchers used different techniques and tools to manually or
automatically remove the artifacts from the EEG signals as shown in Table 16.

The band pass, notch and ICA (Independent Component Analysis) are the
important filters that are used in most of the bipolar disorder studies [174,
175, 57, 117, 178] to remove the noise, interference and physiological and
non-physiological artifacts with 40 Hz to 70Hz frequency range. These filters
remove the artifacts from EEG signals based on the frequency and amplitude
level of the EEG signals (upper and lower level of the signals). The Total
Variation Filtering (TVP) and visual inspection are signal filtering
techniques that are used in some studies [116] and remove noise manually from
the signals.

Table 16: Artifacts and noise filtering approaches in EEG based bipolar disorder detection. Ref |  Artifact Filtering Technique
---|---
[174] |  Bandpass, notch filter and
visual inspection
[175] |  Bandpass filter at 0.15 to 70 Hz
[57] |  Bandpass filter 40HZ
[116] |  Total variation Filtering (TVP)
[117] |  Notch filter at 50HZ
[178] |  Independent component analysis
[177] |  -

####  7.2.2 Neural network based approaches for Bipolar disorder recognition

For the past few decades, neural networks are widely used in EEG based studies
due to their ability of self-learning and producing the output that is not
limited to the input provided to them. Different models of ANN are used in EEG
based bipolar disorder diagnosis and classification as shown in Table 17. The
Multi-layer perceptron (MLP), Feed forward neural network (FFNN),
Probabilistic neural network (PNN) and Artificial neural network (ANN) are the
models that are used in bipolar disorder studies [174, 175] with online
feature extraction mechanism. They achieve a 91.83%, 58.75%, 98.75% and 90%
classification accuracy respectively.

####  7.2.3 Deep learning based approaches for Bipolar disorder recognition

Bipolar disorder is often confused with major depressive disorder and other
mental disorders. It is characterized by persistent depression and mania. The
complications and interruptions in the diagnosis of bipolar disorder delay
effective treatment of patients. Bipolar disorder is misdiagnosed as recurrent
MDD in 60% of patients that seek treatment for depression[184].To overcome all
these limitations and to provide effective diagnosis and treatment to bipolar
patients, there is a need to use deep learning based EEG for bipolar disorder
diagnosis.

Table 17: Neural Network based approaches for EEG based bipolar disorder detection and their accuracy level. Ref |  Model used |  Results
(Accuracy)
---|---|---
[174] |  Multilayer perceptron (MLP) neural network with hidden layer containing 5 neurons |  91.83%
[175] |  3-layered ANN with hidden layers containing 20 neurons |  76%
[57] |  Back-propagation learning algorithm containing one hidden layer with 20 neurons |  89.89%
[116] |  Two layers Feed forward neural network (FFNN) and three layers Probabilistic neural network (PNN) |  58.75%,
98.75%
[117] |  Relative wavelet energy and Feed forward neural network |  98.11%
[178] |  Back-propagation learning algorithm with one hidden layer and 30 neurons |  -
[177] |  Artificial neural network with six neurons in hidden layers |  90%
Table 18: Bipolar disorder type and their criteria defined by DSM-IV. Bipolar Disorder Type |  (DSM-IV) criteria
---|---
Bipolar disorder type I |  In bipolar disorder type I one full episode of mania or diverse episodes of mania and depression can occurs.
Bipolar disorder type II |  In bipolar disorder type II ,no manic episode, minimum one hypomanic episode and many depressive episodes occurs.
Cyclothymic disorder |  Many hypomanic and depressive episodes occurs.
Bipolar disorder not otherwise specified |  Depressive and hypomanic episodes changes rapidly.

##  8 Discussion

Our umbrella review provided an up-to-date overview and synthesis of the
literature of neural networks-based approaches using EEG signals for the
diagnosis of Major Depressive Disorder and Bipolar Disorder. We included data
from different sources of Healthy Brain Network (HBN), PREDICT, pubMed, IEEE
explore, embase, google scholar, research gate and web of science etc. We have
noticed several limitations, which have been discussed with improvement
suggestions as follows:

Few Existing Works: Deep learning approaches for EEG based depression
diagnosis have been found in only 4 to 5 recent articles from 2018-2020 [53,
54, 112]. In case of EEG based bipolar disorder no deep learning based article
exists for diagnosis. Despite the few existing works in deep learning-based
approaches for EEG depression and bipolar disorders analysis, deep learning
seems to be promising in many research areas. It can play a key role in
developing a more accurate biomarkers.

Data Availability: The significant issue regarding the data is its
availability. Most of the techniques reviewed here are on private datasets,
very few public datasets exist for EEG based depression diagnosis due to the
sensitive nature of depression data, and for privacy and confidentiality
reasons as well. In bipolar case no public dataset is available for EEG based
bipolar disorder diagnosis. However, for collective innovation, there should
be some standard public datasets. Open datasets provide new opportunities for
collaborations. Further, these datasets can be useful to evaluate and validate
methodologies/approaches presented by different researchers with standardized
protocols and settings.

AI for a psychiatry revolution: EEG is a non-invasive strong biomarker, mental
health and psychiatry do not benefit as yet. Suggestion: This domain needs to
attract more attention from the scientific community especially the computer
vision researchers with more innovative methods and applications for better
diagnosis of mental disorders.

Deep learning for more complex patterns: The feature extraction phase is very
important in any computer vision pipeline and participate strongly in the
precision and accuracy level of the methodology. The classification is applied
only on extracted features. In fact, the majority of the approaches reviewed
here used the conventional machine learning approaches for feature extraction
that does not predict how many features should be extracted for a high
classification accuracy. Suggestion: Feature extraction phase should be
automatic and predictable for higher classification ratio as in deep learning-
based approaches.

Need for Multimodal approaches: The multimodal approaches of EEG and its
fusion with other modalities are less explored in literature, despite the fact
that multimodal fusion performs better than unimodal approaches in many other
applications [185]. Suggestion: EEG based multimodel deep learning approaches
should be introduced in depression and bipolar disorder detection to enhance
the classification accuracy.

EEG signals and noise: Most of the studies reviewed here use conventional
artifacts removal techniques likewise: visual inspection, notch filter, low
band and high pass filter to sanitize the polluted EEG signals. These
conventional approaches cannot filter the signals accurately and signals still
carry a lot of noise, which yield a negative impact on depression detection/
classification accuracy [186]. Suggestion: The signal pre-processing or
filtering can be automated by using more advanced techniques like deep
learning approaches.

Deep learning and interpretability: The problem of interpretability of the
results of neural networks based approaches still exist. These neural
networks-based approaches can be regarded as black boxes. Without the
interpretability of these algorithm results, it is difficult for a physician
to tell patients about their predicted diagnosis with any genuine reason.
Decision-making in such uncertain situations is a problem of liability, ethics
and beyond pure performance. Therefore, the final decision of algorithm should
be interpretable [187]. Suggestion: The neural networks-based results should
be interpretable to enhance the understanding level and able to work in more
certain scenarios.

Brain Lobes: More than 90% of reviewed studies use the frontal lobe and
left/right hemisphere for depression and bipolar disorder detection as given
in Table 9, and achieve a remarkable classification accuracy. It can be a
strong motivation for the new researchers for future analysis on frontal lobe.

##  9 Conclusion

Mental disorders are highly prevalent and disabling health condition. Numerous
studies explored the use of EEG signals to diagnose the functioning of brain
activity. In this survey paper, the focus is giving to EEG signals as a strong
biomarker for Major Depressive Disorder and Bipolar Disorder. And thus, an
extensive study of the state-of-the-art shallow and deep neural networks based
methods is giving for MDD and BD diagnosis and assessment. While EEG based
methods for MDD diagnosis attracts the attention of the computer vision
community, the EEG based methods for BD diagnosis is less explored in the
literature and need more consideration. The EEG based experimental protocols
and methods could help the scientific community to better understand mental
disorders and to design strong biomarkers for their diagnosis and assessment.
Deep neural networks offer high classification accuracy among the depressed
and healthy control subjects in comparison to shallow neural networks based
methods. Several clinical research issues remain to be addressed
scientifically in this field. Thus, a set of recommendations are addressed in
the discussion section to offer ways to guide the emerging collaborations and
interactions towards the most fruitful outcomes.
In conclusion, the current exhaustive review highlights the considerable
potential of the EEG-based methods for the assessment and monitoring of MDD
and BD. However, despite the good prediction performance of neural networks
based methods, they lack sufficient model explainability needed in
quantitative research and which prevent the community to further develop
reproducible and deterministic protocols and to achieve clinically useful
results. Thus, explainable neural networks based methods are needed for mental
health diagnosis and assessment. Finally, we hope this paper is another step
towards harnessing the full potential of AI for mental health diagnosis.

## Declaration of Competing Interest

The author(s) declare(s) that there is no conflict of interest.

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