Methodological Note: Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications

Abstract

Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects by measuring brain waves and providing a feedback signal. Neurofeedback usually provides the audio and or video feedback. Positive or negative feedback is produced for desirable or undesirable brain activities, respectively. In this review, we provided clinical and technical information about the following issues: (1) Various neurofeedback treatment protocols i.e. alpha, beta, alpha/theta, delta, gamma, and theta; (2) Different EEG electrode placements i.e. standard recording channels in the frontal, temporal, central, and occipital lobes; (3) Electrode montages (unipolar, bipolar); (4) Types of neurofeedback i.e. frequency, power, slow cortical potential, functional magnetic resonance imaging, and so on; (5) Clinical applications of neurofeedback i.e. treatment of attention deficit hyperactivity disorder, anxiety, depression, epilepsy, insomnia, drug addiction, schizophrenia, learning disabilities, dyslexia and dyscalculia, autistic spectrum disorders and so on as well as other applications such as pain management, and the improvement of musical and athletic performance; and (6) Neurofeedback softwares. To date, many studies have been conducted on the neurofeedback therapy and its effectiveness on the treatment of many diseases. Neurofeedback, like other treatments, has its own pros and cons. Although it is a non-invasive procedure, its validity has been questioned in terms of conclusive scientific evidence. For example, it is expensive, time-consuming and its benefits are not long-lasting. Also, it might take months to show the desired improvements. Nevertheless, neurofeedback is known as a complementary and alternative treatment of many brain dysfunctions. However, current research does not support conclusive results about its efficacy.

Full text

143
Basic and Clinical
April 2016. Volume 7. Number 2
Hengameh Marzbani 1, Hamid Reza Marateb 1, Marjan Mansourian 2*
Methodological Note: Neurofeedback: A Comprehensive Review
on System Design, Methodology and Clinical Applications
A B S T R A C T
Key Words:
Brain diseases, Brain
waves, Complementary
therapies,
Electroencephalography,
Neurofeedback
1. Introduction
eurofeedback is not a new concept. It has
been the subject of the study of research-
ers for several decades. Neurofeedback is
a method that assists subjects to control
their brain waves consciously. In fact, the
electroencephalography (EEG) is recorded during the
neurofeedback treatment. Then, its various components
are extracted and fed to subjects using online feedback
loop in the form of audio, video or their combination.
Accordingly, electrophysiological components are sep-
arately demonstrated. As an illustration, the power of a
signal in a frequency band can be shown by a varying
bar graph. During this procedure, the subject becomes
aware of the changes occurring during training and will
be able to assess his/her progress in order to achieve
optimum performance. For instance, the subject tries
to
N
Article info:
Received: 04 April 2015
First Revision: 06 May 2015
Accepted: 27 July 2015
1. Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
2. Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
* Corresponding Author:
Marjan Mansourian, PhD
Address: Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
Tel:+98 (31) 37923256
E-mail: j_mansourian@hlth.mui.ac.ir
Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects
by measuring brain waves and providing a feedback signal. Neurofeedback usually provides
the audio and or video feedback. Positive or negative feedback is produced for desirable or
undesirable brain activities, respectively. In this review, we provided clinical and technical
information about the following issues: (1) Various neurofeedback treatment protocols i.e. alpha,
beta, alpha/theta, delta, gamma, and theta; (2) Different EEG electrode placements i.e. standard
recording channels in the frontal, temporal, central, and occipital lobes; (3) Electrode montages
(unipolar, bipolar); (4) Types of neurofeedback i.e. frequency, power, slow cortical potential,
functional magnetic resonance imaging, and so on; (5) Clinical applications of neurofeedback
i.e. treatment of attention decit hyperactivity disorder, anxiety, depression, epilepsy, insomnia,
drug addiction, schizophrenia, learning disabilities, dyslexia and dyscalculia, autistic spectrum
disorders and so on as well as other applications such as pain management, and the improvement
of musical and athletic performance; and (6) Neurofeedback softwares. To date, many studies
have been conducted on the neurofeedback therapy and its effectiveness on the treatment of
many diseases. Neurofeedback, like other treatments, has its own pros and cons. Although it
is a non-invasive procedure, its validity has been questioned in terms of conclusive scientic
evidence. For example, it is expensive, time-consuming and its benets are not long-lasting.
Also, it might take months to show the desired improvements. Nevertheless, neurofeedback is
known as a complementary and alternative treatment of many brain dysfunctions. However,
current research does not support conclusive results about its efcacy.
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

144
improve the brain patterns based on the changes that oc-
cur in the sound or movie. Neurofeedback treatment pro-
tocols mainly focus on the alpha, beta, delta, theta, and
gamma treatment or a combination of them such as alpha/
theta ratio, beta/theta ratio, etc. (Dempster, 2012; Vernon,
2005). However, the most commonly used protocols are
alpha, beta, theta, and alpha/theta ratio. In this review pa-
per, we discussed various technical and clinical details of
different neurofeedback treatment protocols.
2. Various Frequency Components
Activities of cerebral neurons have rich information
about neuronal activities. When neurons are activated,
they produce electrical pulses. By placing electrodes on
the scalp, the electrical activity of the brain, known as
EEG, can be recorded. In turn, EEG is generated by a spe-
cic type of synchronous activity of neurons which are
known as pyramidal neurons and the electrical output is
thus reected in the following areas of the skin where the
electrodes are located. Different patterns of electrical ac-
tivity, known as brain waves, could be recognized by their
amplitudes and frequencies. Frequency indicates how fast
the waves oscillate which is measured by the number of
waves per second (Hz), while amplitude represents the
power of these waves measured by microvolt (µV).
Different frequency components are categorized into
delta (less than 4 Hz), theta (4-8 Hz), alpha (8-13 Hz),
beta (13-30 Hz), and gamma (30-100 Hz) where each
represents a particular physiological function. In sum-
mary, delta waves are observed in the EEG signal when
a person is asleep, theta waves when a person is sleepy,
alpha waves when a person is relaxed and his/her mus-
cles are loose but he/she is awake, beta waves when a
person is alert and gamma waves are observed when a
person is trying to solve a problem (Table 1). However,
there are differences in dening the exact range of fre-
quency components in different studies.
These frequency components have subsets. For exam-
ple, sensorimotor rhythm (SMR) frequency bands (13-15
Hz) are related to the sensorimotor rhythm and entitled
as low beta. Some studies claimed that alpha rhythm has
two subsets: lower alpha in the range of 8-10 Hz and up-
per alpha in the range of 10-12 Hz. Whereas some studies
indicate that the alpha rhythm has 3 subsets. These de-
nitions indicate that high and low alpha exhibit different
behaviors and performances. It is believed that lower al-
pha is related to remembering action in semantic memory
which is not the case for high alpha (Dempster, 2012).
3. EEG Electrode Placement
Electrodes (placed on the scalp) can record those corti-
cal activities of the brain regions that are close to them.
Electrode System 10-20 is a method for standardizing
areas of the skull and comparing data. The term “10-20”
refers to the placement of electrodes over 10% or 20%
of the total distance between specied skull locations.
Studies have shown that these placements correlate
with the corresponding cerebral cortical regions. Of 21
electrodes, 19 are used for recording cortical areas and
2 other electrodes as reference electrodes (Figure 1).
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Table 1. Specic brainwaves with their characteristics.
Common brainwave frequency Frequency range (Hz) General characteriscs
Delta 1-4 Sleep, repair, complex problem solving, unawareness, deep-unconsciousness
Theta 4-8 Creavity, insight, deep states, unconsciousness, opmal meditave state,
depression, anxiety, distracbility
Alpha 8-13 Alertness and peacefulness, readiness, meditaon, deeply-relaxed
Lower alpha 8-10 Recalling
Upper alpha 10-13 Opmize cognive performance
SMR (sensorimotor rhythm) 13-15 Mental alertness, physical relaxaon
Beta 15-20 Thinking, focusing, sustained aenon, tension, alertness, excitement
High beta 20-32 Intensity, hyperalertness, anxiety
Gamma 32-100 or 40 Learning, cognive processing, problem solving tasks, mental sharpness, brain
acvity, organize the brain
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

145
Basic and Clinical
April 2016. Volume 7. Number 2
The skull regions are named using letters and numbers.
Letters correspond with the brain regions and numbers
to the hemisphere of the brain or the locations of this
hemisphere. The letters F, P, T, O, and C are related to
frontal, parietal, temporal, occipital, and central areas,
respectively. Odd/even numbers are associated with the
left/right side of the brain region. The letter z is used
as PZ suggests that scalp location falls along the central
line running between the nasion and the inion. FP1 and
FP2 are respectively related to the left and right poles of
the forehead. Also A1 and A2 are the left right regions of
vestibular (ear) region that are two common sites for the
placement of reference and ground electrodes (Figure 1)
(Dempster, 2012; Evans & Abarbanel, 1999).
Traditionally, two types of unipolar and bipolar mon-
tage are used in the neurofeedback treatment. In uni-
polar mode, the active electrode is placed on the skull
and the recorded signal by the active electrode is com-
pared to the second electrode entitled as the reference
electrode. The activity of the active electrode minus the
activity of the reference electrode represents the brain
activity at the active electrode.
On the other hand, in the bipolar mode, two active elec-
trodes are used that are separately placed on the skull. The
difference between the recorded signals by these 2 elec-
trodes, is the basis of the neurofeedback (Demos, 2005;
Dempster, 2012). One of the advantages of the bipolar re-
cording is the common mode rejection that occurs during
the recording procedure. It means that any external artifact
occurring at both channels and at the same time, its ampli-
tude and phase are subtracted and the spatial selectivity is
improved. For example, eye roll and blink artifacts could
be reduced in this way (Evans & Abarbanel, 1999).
Neurologists have observed that lesions occurring in
specic regions of the brain produce specic symptoms
mostly related to these regions. For example, frontal
lobes, FP1 , FP2 , FPZ , FZ , F3 , F4 , F7 are responsible for
immediate and sustained attention, time management,
social skills, emotions, empathy, working memory, ex-
ecutive planning, moral ber or character. Each region
represents a specic feeling or task; Thus identication
of these areas provides the best and the most accurate
neurofeedback treatment. Parietal lobes, PZ , P3 and P4,
solve problems conceptualized by the frontal lobes.
Complex grammar, naming of the objects, sentence con-
struction, and mathematical processing are identiable
to the left parietal lobe while map orientation, spatial
recognition, and knowing the difference between right
and left are entirely functions of the right parietal lobe.
Temporal lobes, T3 , T4 , T5 and T6 have various functions.
Left hemisphere functions are associated with reading
(word recognition), memory, learning and a positive
mood, while right hemisphere functions are related to
music, anxiety, facial recognition, and sense of direction.
On the other hand, visual memories, accurate reading
and traumatic memories accompanying visual ash-
backs are usually processed in the occipital lobes, O2 ,
O1 and . The other functions of this lobe include helping
to locate objects in the environment, seeing colors and
recognizing drawings and correctly identifying objects,
reading, writing, and spelling. Sensory and motor (sen-
sorimotor) cortex, CZ , C3 and C4 have functions of con-
scious control of all skeletal movements such as typing,
playing musical instruments, handwriting, operation of
complex machinery, speaking, and the ability to recog-
nize where bodily sensations originate.
Neurologists have mentioned that the motor cortex
helps the cerebral cortex to encode both physical and
cognitive tasks. Therefore, subjects who have trouble
seeing the logical sequence of cognitive tasks may ben-
et from neurofeedback training along the left hemi-
sphere sensorimotor cortex (C3). Training along the
right hemisphere sensorimotor cortex (C4) may invoke
feelings, emotions, or calmness. Training at the median
or may facilitate a mixed response. The subjects who
suffer from epilepsy are usually trained along the sen-
sorimotor cortex (C3) to increase SMR. Also, training
along the sensorimotor cortex could be applied for the
treatment of stroke, epilepsy, paralysis, ADHD, and dis-
orders of sensory/motor integration (Table 2) (Demos,
2005).
Generally, electrodes are placed in a way that a particu-
lar EEG channel is located on one brain side (Bauer &
Pllana, 2014). For instance, low beta and beta are trained
on the right (C4) and left (C3) brain side, respectively.
If they were switched to the opposite brain side, unde-
sirable results could be obtained. For example, training
low beta wave on the left side will result in a depletion
Figure 1. The 10-20 electrode placement system and the
name of the skull regions.
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

146
Table 2. Brain lobes with their functions and areas (Demos, 2005).
Sites Funcons Consideraons
Parietal lobes Pz , P3 , P4
LH: Problem solving, math, complex
grammar, aenon,
associaon
RH: Spaal awareness,
Geometry
Dyscalculia sense of direcon learning
disorders
Frontal lobes FP1, FP2 , FPZ , FZ , F3 , F4 , F7 , F8
LH: Working memory, concentraon,
Execuve planning, posive emoons.
RH: Episodic memory,
social awareness
Frontal poles: aenon judgment
LH: Depression
RH: Anxiety, fear, execuve planning, poor
execuve funconing
Temporal lobes T3 , T4 , T5 , T6
LH: Word recognion, reading, language,
memory
RH: Object recognion, music, social
cues
Facial recognion
Anger, rage, dyslexia, long-term memory,
closed head injury
Occipital lobes OZ , O1 , O2
Visual learning,
reading, parietal- temporal-occipital
funcons
Learning disorders
Sensorimotor cortex CZ , C3 , C4
LH: Aenon, mental processing,
RH: Calmness, emoon,
Empathy
Combined: Fine motor
skills, manual
dexterity, sensory
and motor integraon
and processing
Paralysis (stroke), seizure disorder, poor
handwring, ADHD symptoms
Cingulate
gyrus FPZ , FZ , CZ , PZ , OZ
Mental exibility, cooperaon,
aenon, movaon,
morals
Obsessions, compulsions, cs, perfecon-
ism, worry, ADHD symptoms, OCD
& OCD spectrum
Broca’s area F7 , T3Verbal expression Dyslexia, poor spelling, poor reading
Le hemisphere All odd numbered sites
Logical sequencing,
detail oriented, language abilies, word
retrieval,
uency, reading,
math, science,
problem solving,
verbal memory
Depression
(underacvaon)
Right hemisphere All even numbered sites
Episodic memory
encoding, social awareness, eye
contact, music,
humor, empathy,
spaal awareness,
art, insight, intuion,
non-verbal memory,
seeing the whole picture
Anxiety
(overacvaon)
Abbreviations: LH, Left hemisphere, RH: Right hemisphere, AHHD: Attention decit hyperactivity disorder, OCD: Obsessive
compulsive disorder.
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

147
Basic and Clinical
April 2016. Volume 7. Number 2
of mental energy instead of improvements in concentra-
tion. Thus, the location of the EEG electrodes during the
neurofeedback procedure is important (Evans, 2007).
4. Types of Neurofeedback
There are 7 types of Neurofeedback for the treatment
of various disorders:
1) The most frequently used neurofeedback is fre-
quency/power neurofeedback. This technique typically
includes the use of 2 to 4 surface electrodes, sometimes
called “surface neurofeedback”. It is used to change the
amplitude or speed of specic brain waves in particular
brain locations to treat ADHD, anxiety, and insomnia.
2) Slow cortical potential neurofeedback (SCP-NF)
improves the direction of slow cortical potentials to
treat ADHD, epilepsy, and migraines (Christiansen,
Reh, Schmidt, & Rief, 2014).
3) Low-energy neurofeedback system (LENS) deliv-
ers a weak electromagnetic signal to change the pa-
tient’s brain waves while they are motionless with their
eyes closed (Zandi Mehran, Firoozabadi, & Rostami,
2014). This type of neurofeedback has been used to treat
traumatic brain injury, ADHD, insomnia, bromyalgia,
restless legs syndrome, anxiety, depression, and anger.
4) Hemoencephalographic (HEG) neurofeedback pro-
vides feedback on cerebral blood ow to treat migraine
(Dias, Van Deusen, Oda, & Bonm, 2012).
5) Live Z-score neurofeedback is used to treat insom-
nia. It introduces the continuous comparison of vari-
ables of brain electrical activity to a systematic database
to provide continuous feedback (Collura, Guan, Tarrant,
Bailey, & Starr, 2010).
6)
Low-resolution electromagnetic tomography (LORE-
TA) involves the use of 19 electrodes to monitor phase,
power, and coherence (Pascual-Marqui, Michel, & Lehm-
ann, 1994). This neurofeedback technique is used to treat
addictions, depression, and obsessive-compulsive disorder.
7) Functional magnetic resonance imaging (fMRI) is
the most recent type of neurofeedback to regulate brain
activity based on the activity feedback from deep sub-
Table 3. Summary of studies using alpha protocol training.
Site of treatment Enhance/inhibit Number of sessions Outcome
(Allen, Harmon-Jones, &
Cavender, 2001) F3 , F4Enhance alpha (8-13 Hz) 5
Impact of self-reported emo-
onal responses and facial EMG
(Angelakis et al., 2007) FO3
Enhance peak alpha (8-13
Hz) 31-36
Improve cognive processing
speed and execuve funcon
(Hanslmayr, Sauseng,
Doppelmayr, Schabus, &
Klimesch, 2005)
F3 , F4 , FZ , P3 , P4 , PZEnhance upper alpha 1Improvement in cognive
performance
(Hardt & Kamiya, 1978) OZ , O1 , C3Enhance alpha (8-13 Hz) 7Decrease anxiety
(Hord, Tracy, Lubin, &
Johnson, 1975) O2Enhance alpha
Help maintain performance
such as counng and auditory
discriminaon
(Markovska-Simoska et
al., 2008) F3-O1 , F4 -O2
Enhance individual upper
alpha 20 Increasing the quality of musical
performance
(Marndale & Armstrong,
1974) O2, P4
Reducon alpha (7-13) 1High creave
(Plotkin & Rice, 1981) OZEnhance alpha 5-7 Decrease anxiety
(Regestein, Buckland, &
Pegram, 1973) Parietal-occipital Enhance alpha (8-13 Hz) 2Decrease sleep need
(Schmeidler & Lewis, 1971) Right occipital both 2Mood changes
(Zoefel, Huster, & Her-
rmann, 2011) P3 , PZ , P4 , O1 , O2
Enhance individual upper
alpha 5Enhancement of cognive
performance
Abbreviation: EMG, Electromyogram.
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

148
cortical areas of the brain (Hurt, Arnold, & Lofthouse,
2014; Lévesque, Beauregard, & Mensour, 2006a).
5. Various Treatment Protocols
5.1. Alpha protocol
The alpha wave of the brain is usually associated with
alert relaxation (Evans & Abarbanel, 1999). The alpha
mood is described as a calm and pleasant situation.
All alpha frequencies describe creative activity of the
brain, so that it is used in the process of relaxation (re-
laxing the muscles), which eventually leads to sleep;
Such waves emerge and expand rapidly on the skin.
The evidence shows that alpha waves increases during
meditation.
Alpha training is usually used for the treatment of
various diseases such as pain relief (by 9 Hz simula-
tion), reducing stress and anxiety (by 10 and 30 Hz
simulation), memory improvement, improving mental
performance, and treatment of brain injuries (by 10.2
Hz simulation). Various studies have been performed
on the alpha protocol (Table 3). The most common fre-
quency bandwidth for the alpha treatment is 7-10 Hz
frequency range, which is used for meditation, sleep,
reducing stress and anxiety. Also frequency of 10 Hz
causes deep muscle relaxation, pain reduction, regulat-
ing breathing rate, and decreasing heart rate (Demp-
ster, 2012; Vernon, 2005).
5.2 Beta protocol
Beta activity is a good indicator for mental perfor-
mance and inappropriate beta activity represents men-
tal and physical disorders like depression, ADHD, and
insomnia (Egner & Gruzelier, 2004). Beta brain waves
are associated with conscious precision, strong focus,
and ability to solve problems. Medications that are
used to stimulate alertness and concentration such as
Ritalin and Adderall also cause the brain to produce
beta brainwaves.
Beta training is used to improve focus and attention
(simulation of increased beta 12-14 Hz), improve the
reading ability (simulation of 7-9 Hz), and introduce
positive changes in school performance. It also im-
proves the computational performance, cognitive pro-
cessing, reduction of worries, over-thinking, obsessive
compulsive disorder (OCD), alcoholism, and insomnia
(simulation of 14-22 Hz and 12-15 Hz). Meanwhile,
this type of neurofeedback improves sleep cognitive
Table 4. Summary of studies using beta protocol training.
Site of treatment Enhance/inhibit Number of sessions Outcome
(Rasey, Lubar, McIntyre,
Zouto, & Abbo, 1995)
Central-posterior region
(CPZ , PCZ )
Enhance beta (16-22 Hz) and
inhibit high theta and low alpha 20 Improvement in aenonal
performance
(Egner & Gruzelier, 2001)
(12-15 Hz) at right central
region (C4) and (15-18 Hz)
at the le central region
(C3)
Enhance low beta (12-15 and 15-
18 Hz), inhibing theta (4-7 Hz)
and high beta (22-30 Hz)
10 Successful enhancement of
aenonal performance
(Vernon et al., 2003) CZ
Enhance low beta (12-15 Hz),
inhibing theta (4-8 Hz) and high
beta (18-23 Hz)
15 Enhance cognive perfor-
mance
(Egner & Gruzelier, 2004) CZ
Enhance SMR (12-15 Hz) and
inhibit theta (4-7 Hz) and high
beta (22-30 Hz)
10 Improve perceptual
sensivity
(Egner & Gruzelier, 2004) CZ
Enhance low beta (15-18 Hz),
inhibing theta (4-7 Hz) and high
beta (22-30 Hz )
10 Increase corcal arousal
(Vernon et al., 2003) CZ
Enhance SMR (12-15 Hz) and
inhibit theta (4-7 Hz) and high
beta (18-22 Hz)
8Increased recall in seman-
c working memory
(Lubar, Swartwood, Swart-
wood, & O’Donnell, 1995) FCZ , CPZ
Enhance beta (16-20 Hz) and
inhibit theta 40
Reducon of inaen-
on, hyperacvity and
impulsivity
(Fuchs, Birbaumer, Lutzen-
berger, Gruzelier, & Kaiser,
2003)
C3 , C4
Enhance beta (15-18 Hz) and
SMR (12-15), inhibit theta 36 Improvement in aenon
and intelligence
(Heinrich, Gevensleben, &
Strehl, 2007) C4, CZEnhance SMR and inhibit theta Treatment epilepsy disor-
der and ADHD
(Heinrich, Gevensleben, &
Strehl, 2007) CZ , C3
Enhance beta (13-20 Hz) and
inhibit theta Treatment ADHD
Abbreviation: SMR, Sensorimotor rhythm.
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

149
Basic and Clinical
April 2016. Volume 7. Number 2
performance as well as reducing fatigue and stress (sim-
ulation of light and sound of beta) (Table 4). The beta
waves in the range of 12-15 Hz (SMR) reduce anxiety,
epilepsy, anger and stress (Egner & Gruzelier, 2004;
Vernon, 2005).
5.3. Alpha/theta protocol
Alpha/theta is an indicator between awareness and
sleep. Alpha/theta training is one of the most popular
neurofeedback trainings for stress reduction (Gruzelier,
2009; Raymond, Varney, Parkinson, & Gruzelier, 2005).
Also, this treatment is used for deep levels of depression,
addiction, anxiety while it increases creativity, relaxation,
musical performance, and promotes healing from trauma
reactions. The electrodes are usually located on O1 , O2 ,
CZ and PZ . Alpha/theta frequency range is 7-8.5 Hz with
the typical value of 7.8 Hz. This treatment is done under
eyes-closed condition that increases the ratio of theta to
alpha waves using auditory feedback (Demos, 2005; Eg-
ner & Gruzelier, 2003; Thompson & Thompson, 2003).
The summary of the studies using alpha/theta protocol
training are presented in Table 5.
5.4. Delta protocol
Delta waves are the slowest brain waves, which are as-
sociated with stages 3 and 4 of the sleep (Sürmeli & Er-
tem, 2007). They represent increased comfort, reduced
pain, and sleep. Thus, they are used to alleviate headaches,
traumatic brain injury, learning disorders, and to treatment
hard and sharp contraction of muscles (by simulation of
1-3 Hz delta wave). They also reduce concerns and im-
prove sleep (Vernon, 2005).
5.5. Gamma protocol
Gamma waves have the highest frequency, and they are
associated with cognitive processing and memory (Staufen-
biel, Brouwer, Keizer, & Van Wouwe, 2014). Thus, when
these waves are faster, the speed of recalling memory is
faster. Gamma waves are fast rhythms that are responsible
for the brain’s neural connections and data transfer to the
outside world.
They are mainly observed in the hippocampus (an area
of the brain which is responsible for converting short-term
to long-term memory). Also, these rapid rhythms are ob-
served in sudden attacks like seizure and spasm. Hence,
gamma training is used for promoting cognition, mental
sharpness, brain activity, and problem-solving tasks. It
not only improves poor calculation, but also organizes the
brain, improves the speed of information processing, short-
term memory, and reduces the number of migraine attacks
(Hughes, Vernon, 2005).
5.6. Theta protocol
Theta brain waves are related to a number of brain ac-
tivities such as memory, emotion, creativity, sleep, med-
itation, and hypnosis. These waves are also associated
with the rst phase of sleep when the sleep is light and
the person easily wakes up. Theta treatment reduces anx-
iety, depression, day dreaming, distractibility, emotional
disorders, and ADHD (Beatty, Greenberg, Deibler, &
O’Hanlon, 1974; Vernon, 2005).
5.7. Low frequency versus high frequency training
Basically, there are two classical directions in neurofeed-
back training. It is either focusing on low frequencies (al-
pha or theta) to strengthen relaxation and focus (Gruzelier,
2009) or emphasizing on high frequencies (low beta, beta,
and theta) for reinforcing activation, organizing, and inhib-
iting distractibility (Ros et al., 2009).
A suitable comparison between these two directions
could be found at Thomas F. Collura (2000), and Kropotov
(2010) studies. For example, in the former strategy eyes
are closed while in the later one, eyes are open. Also, chil-
Table 5. Summary of studies using alpha/theta protocol training.
Site of treatment Enhance/inhibit Number of sessions Outcome
(Raymond, Sajid, Parkin-
son, & Gruzelier, 2005) P4
Enhance theta (4-7 Hz) over
alpha (8-11 Hz) 10 Improvement in arsc
performance
(Egner & Gruzelier, 2003) C4 , C3 , PZ
Enhance theta (5-8 Hz) over
alpha (8-11 Hz) 10 Improvement of music
performance
(Gruzelier, 2009) Enhance theta (4-7 Hz) over
alpha ( 8-11 Hz)
Half-hour sessions, twice a
week
Enhancement of arsc
performance and mood
(Gruzelier, 2009) Enhance theta (4-7 Hz) over
alpha ( 8-11 Hz) 10 Enhancement of music
performance
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

150
dren are not involved in the rst strategy while children
and adult could undergo the second training procedure.
6. Clinical Applications of Neurofeedback
Training in the Treatment of Diseases and
Disorders
Antisocial behavior of individuals, have an undesirable
impact on the society. In recent years, with advances in
brain science, the cause of abnormal brain function and
mental illness has been attributed to the low activity of the
anterior brain lobe that presents itself in different types of
psychological damages (Gil, 2009). The neurofeedback
training has been widely used in the treatment of many dis-
eases and disorders; some of which are mentioned below.
6.1. Attention decit/hyperactivity disorder
Evidence suggests that the malfunction of the right fron-
tal lobe, is the cause of attention decit/hyperactivity disor-
der (ADHD) (Hynd et al., 1991). The resulting symptoms
are inattention, distractibility, hyperactivity, and extreme
dispassionateness. Neurofeedback therapy is a rehabilita-
tion approach for its treatment. Its goal is to normalize the
behavior without dependence on medications or behav-
ioral therapy. For a long time, such drugs as Ritalin, Con-
certa, and Dexedrine have been used for treating ADHD.
But, recent research showed that these drugs do not have
any effect on the clinical treatment of ADHD on some of
children. Also, these drugs have the side effects such as
anxiety, irritability, abdominal pain, decreased appetite, in-
somnia, and headache. However,
using neurofeedback is
associated with their long-term improvement (Yan et al.,
2008). Studies showed that people with ADHD disorder
have slower brain wave activity (theta) and less beta ac-
tivity compared to normal people.
In ADHD, the goal is to decrease the brain activity in
the theta band and to increase its activity in the beta band
(or to decrease theta/beta ratio) at the vertex (electrode)
(Heinrich, Gevensleben, & Strehl, 2007). This treat-
ment
is effective in reducing hyperactivity; Increasing fo-
cus, grades, and parental consent from children’s behavior;
and improving indicators of sustained attention (Gnecchi,
Herrera Garcia, & de Dios Ortiz Alvarado, 2007; Karimi,
Haghshenas, & Rostami, 2011; Wang & Sourina, 2013).
The studies on the neurofeedback treatment of ADHD
in children are listed in Table 6. According to this Table,
theta/beta protocol and the area for locating the EEG elec-
trode are the most commonly used neurofeedback strategy
in ADHD treatment.
6.1.1. Schizophrenia
Schizophrenia is known as the most unbearable mental
illness (Surmeli, Ertem, Eralp, & Kos, 2012). People with
schizophrenia have the illusion of auditory disorders, rest-
lessness, non-exible muscles, confusion, delirium, and
depression. Based on several papers on the treatment of
schizophrenia, Minnesota Multiphasic Personality Inven-
tory (MMPI) and Test of Variables of Attention (TOVA),
positive effect of neurofeedback training on the treatment
Table 6. Summary of neurofeedback treatment studies on ADHD.
Site of treat-
ment
Neurofeedback
Protocol
Number of
sessions
The age range
(year) Outcome
(Linden, Habib, & Rado-
jevic, 1996) CZ
Enhance beta
Inhibit theta 20 5-15
Improvement in mental
funcons
and accuracy
(Palsson et al., 2001.) CZTheta/beta, SMR 40 9-13 Improvement in eects
of ADHD
(Orlandi, 2004) CZTheta/beta, SMR 40 9-11 Improvement in aen-
on, focus and memory
(Lévesque, Beauregard,
& Mensour, 2006b) CZTheta/beta, SMR 40 8-12 Improving performance of
anterior cingulate cortex
(Leins et al., 2007) CZTheta/beta 30 8-13
Improvement in aen-
on, hyperacvity and
distracon
(Gevensleben et al.,
2009) CZTheta/beta 18 9-12
Improvement in com-
bined treatment of neuro-
feedback protocols
(Perreau-Linck, Lessard,
Lévesque, & Beauregard,
2010)
CZTheta/SMR 40 8-13 Improvement in the ef-
fects of ADHD
Abbreviations: ADHA: Attention decit hyperactivity disorder, SMR: Sensorimotor rhythm.
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

151
Basic and Clinical
April 2016. Volume 7. Number 2
of this disease is expressed in such a way that the person
with schizophernia is able to adjust his/her brain activity
on specic frequencies (McCarthy-Jones, 2012; Surmeli et
al., 2012; Wenya et al., 2012; Gil, 2009).
6.1.2. Insomnia
Insomnia is known as an epidemic disorder. The rst
change observed in patients, who are treated with neuro-
feedback training is the change and improvement in their
sleep pattern. Hence, the neurofeedback training is used in
the treatment of sleep disorders (Hammer, Colbert, Brown,
& Ilioi, 2011). For example, the following process is used
to improve sleep. One electrode is placed on and the treat-
ment is done for 30 minutes at a frequency of 15-18 Hz.
This method makes the waking state, alert and active and
assist people in waking up faster. The calmness treatment
is done at frequencies of 12-15 Hz and in location. Using
neurofeedback helps the people who normally take about
an hour in order to prepare their body and mind for sleep,
go to sleep faster.
6.1.3. Learning disabilities, dyslexia and dyscalculia
Neurofeedback has created a big change in the treat-
ment of these disorders. These disorders are more
common at school age and patients with dyslexia have
trouble in reading and spelling the characters (Breteler,
Arns, Peters, Giepmans, & Verhoeven, 2010). People
having dyscalculia, are unable to understand and solve
math problems. These disorders are treated with in-
creased alpha wave activity using neurofeedback (Wang
& Sourina, 2013).
6.1.4. Drug addiction
Studies have shown that neurofeedback training is a good
way to quit drug addiction whereas long-term use of the
drug has a profound effect on the individual’s EEG. Temp-
tation and craving of drugs could be reduced by neurofeed-
back in patients addicted to cocaine (Horrell et al., 2010).
This treatment can also be used to treat alcoholism and ad-
diction to computer games (Moradi et al., 2011).
6.1.5. Enhancing the performance of athletes, artists,
and surgeons
Studies have shown that professional athletes have dif-
ferent patterns of brain activity compared to those of the
beginners. Recognition of the status of the professional’s
EEG before and during performance, provides a rationale
for the use of neurofeedback training to create or emulate
these patterns and to improve the performance of unprofes-
sional individuals (Vernon, 2005). In fact the purpose of
neurofeedback on athletes is improving the athlete’s psy-
chomotor and self-regulation ability, their condence, and
subsequent performance in important competitions of the
year (Edmonds & Tenenbaum, 2011).
6.1.6. Autistic spectrum disorder
Autistic spectrum disorder (ASD) is a neurodevelopmen-
tal disorder with challenges that maintain in adulthood.
Children with autism have difculty in functions such
as social interaction, verbal and nonverbal communica-
tion, behavior and interests. ASD may be associated with
emotional problems, mental retardation, or seizure disor-
ders. These children may also have extreme sensitivity to
sounds and smells. Also, children with autism may show
idiosyncratic behaviors, obsessive rumination, poor social
interrelatedness, and at affect. Researchers found out that
individuals with autism differ from normative samples with
regard to impediments in empathy or theory of mind (TOM)
tasks, weak central coherence, and executive functioning.
One of the primary symptoms of ASD is a qualitative im-
pairment in social interactions related to mutual interest,
understanding others’ intentions, empathy, emotional reci-
procity, and the underlying concepts of TOM. Empathizing
decits are consistent with problems in reciprocating com-
munication, difculty in predicting thoughts and feelings
of others, interpreting abstract emotions of others, and an
appearance of social insensitivity. Individuals with autism
are also often seen to have interest in system details and
pursue careers in engineering, construction, clocks, ma-
chines, puzzles, or computers, which are often obsessive
interests in ASD (Lucido, 2012).
There are several diagnostic tools designed to show ab-
normalities in brain’s function for autism. They are (1)
High-beta activity related to anxiety; (2) The high activity
of delta/theta corresponding with the slow cortex, lack of
attention, impulsivity and hyperactivity; and (3) Abnormal
EEG/seizure activity. High beta type is the most common
one seen among children with ASD (approximately 50-
60% of individuals with ASD) (Coben, Linden, & Myers,
2010; Kouijzer, van Schie, de Moor, Gerrits, & Buitelaar,
2010). The goal of neurofeedback in children with autism
is to inhibit theta-alpha ratio while enhancing beta wave.
Efcacy of neurofeedback in children diagnosed with au-
tism has been well researched in qualitative case studies
summarized in Table 7.
6.1.8. Epilepsy
In about one-third of patients with epilepsy, medical
treatment is ineffective. Neurofeedback training was
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

152
shown to be a good alternative treatment for these pa-
tients. Research has been focused on increasing SMR
(12-15 Hz) and synchronous or asynchronous reduction
of slow rhythms (4-7 Hz) for diagnosing this disorder.
Also, observing low-amplitude gamma wave after sur-
gery is a good sign for the improvement of epilepsy.
The results of studies on the treatment of epilepsy by
neurofeedback indicated that continuous SMR treatment
Table 7. Summary of neurofeedback treatment studies on autistic spectrum disorder (ASD).
Site of treatment Enhance/inhibit Number of sessions Outcome
(Cowan & Markham, 1994) Parietal and occipital
lobes
Enhance (16-20 HZ)
Inhibit ( 4-10 HZ) 21 Improvement in focus, aen-
on, and relax
(Thompson & Thompson,
2003)
Sensorimotor cortex
(C2, C4)
Enhance (13-15 Hz)
Inhibit (3-10 Hz) 40-100
Improvement in neuro-
psychological funconing,
improved educaonal perfor-
mance, decrease anxiety and
impulsivity
(Sichel, Fehmi, & Goldstein,
1995)
Sensorimotor strip and
parietal lobe
Enhance SMR (12-15 Hz)
Inhibit theta (4-8 Hz) 31
Improvement in sleep, social
behaviors
Increase in appropriate eye
contact
Reducon in self-simulaon
(Othmer, 2007) P4 , T4 , T3 , F2 , FP1 Enhance SMR (12-15 Hz) 28-100
Decreased need for special
educaon services and
ausm symptoms
(Thompson, Thompson, &
Reid, 2010) Central sites
Enhance SMR (12-15 or
13-15 Hz)
Inhibit theta (3-7 Hz) and
beta (23-35 Hz)
40-60
Improvement in intelligence
tesng and psychological
assessments
(Cowan & Markham, 1994)
Enhance beta (16-20 Hz)
Inhibit theta-alpha (4-10
Hz)
Improvement in ausc
behaviors, social, academic
funconing and aenon
Abbreviation: SMR: Sensorimotor rhythm.
Table 8. Summary of neurofeedback treatment studies on epilepsy that the results was the remission.
Neurofeedback
protocol Measuring results Length of treatment The age range (year)
(Sterman, Macdonald, &
Stone, 1974) SMR (11-15 Hz) Seizure frequency,
EEG 6-18 months 6-46
(Kaplan, 1975) SMR The number of seizures
per day 20-25 weeks 20-30
(Lubar & Bahler, 1976) SMR The number of seizures 80-260 days 12-29
(Kuhlman & Allison, 1977) SMR (4-9 Hz) The number of seizures,
EEG 24 sessions 17-42
(Sterman & Macdonald,
1978) SMR
The number of seizures
per month,
EEG
12 months 10-40
(Co, Pavloski, & Black,
1979) SMR The number of seizures
per month 210 days 16-31
(Quy, Hu, & Forrest,
1979) SMR The number of seizures
per week, EEG 12 months 23-49
(Lubar et al., 1981) SMR Seizure frequency,
EEG 10 months 13-52
(Tozzo, Elfner, & May,
1988) SMR The number of seizures 5 weeks 18-29
Abbreviation: EEG, Electroencephalogram, SMR, Sensorimotor rhythm.
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

153
Basic and Clinical
April 2016. Volume 7. Number 2
reduces the rate of seizures in severe and uncontrolled
epilepsy (Table 8) (Hughes et al., 2009; Walker, 2010).
6.1.9. Depression
Depression is associated with hypometabolism in the
cingulate and occasionally in the frontal cortex, insula,
anterior temporal cortices, amygdala, basal ganglia,
and thalamus. Along with the frontal electrophysiology
ndings in depression, there seems to be an inverse re-
lationship between frontal alpha asymmetry and pari-
etal asymmetries. More specically, depressed patients
who do not have signicant anxiety, appear to have de-
creased right parietal activation (alpha wave at P4). Neu-
rofeedback training is used to increase alpha and theta,
while inhibit faster beta frequencies, produces signi-
cant improvements in depression (Budzynski, 2009a;
Hurt et al., 2014).
6.1.10. Anxiety
In clinical medicine, anxiety is often dened, at least in
part, as high level of muscle tension. Researchers found
out that decreasing frontal electromyogram (EMG) levels
by EMG biofeedback could alleviate both generalized and
specic anxiety patterns. It was believed that anxiety inhib-
its alpha waves, so alpha training would relieve the anxiety
(Budzynski, 2009a; Demos, 2005; Moore, 2000).
6.1.11. Pain management
Pain is considered a symptom associated with physical
damage, purportedly having an objective element connect-
ed with the sensation. Neurofeedback methodology pro-
poses that by teaching self-regulation, a patient can reduce
or even eliminate pain sensations. Studies suggested that
brain changes its functional organization at the level of the
somatosensory cortex in chronic pain patients. Research-
ers recommend the use of biofeedback/neurofeedback for
pain management. Biofeedback protocols are designed to
address the peripheral correlation of arousal, such as tem-
perature, heart rate variability, and muscle tension while
neurofeedback directly affects the processing of pain per-
ception (Ibric & Dragomirescu, 2009).
6.2. Other uses of neurofeedback
Other applications of neurofeedback include the recov-
ery from an injury and stroke problems, improvement of
memory by increasing alpha activity (Escolano, Aguilar,
& Minguez, 2011; Klimesch, 1999; Vernon, 2005; Wenya
et al., 2012), treatment of headache and migraines (Walk-
er, 2011), distraction, confusion, attention problems, with-
drawal (Escolano et al., 2011; Gnecchi et al., 2007), health
promotion (Escolano, Olivan, Lopez-del-Hoyo, Garcia-
Campayo, & Minguez, 2012), treatment of mental illness
(Heinrich, Gevensleben, & Strehl, 2007), eating disorders
(Bartholdy, Musiat, Campbell, & Schmidt, 2013) Parkin-
son disease (Rossi-Izquierdo et al., 2013), bromyalgia,
restless legs syndrome (Hurt et al., 2014), obsessive com-
pulsive disorder (Sürmeli & Ertem, 2011), and obsession
(Markovska-Simoska, Pop-Jordanova, & Georgiev, 2008;
Surmeli & Ertem, 2011). Meanwhile, artists and surgeons
use neurofeedback to improve their music performance
(Markovska-Simoska et al., 2008) and microsurgical op-
erations (Ros et al., 2009), respectively.
Alpha-EEG/EMG biofeedback is capable of increas-
ing voluntary self-regulation and the quality of musical
performance (Budzynski, 2009b; Markovska-Simoska
et al., 2008).
7. Neurofeedback Softwares
Brain-computer interface systems (BCI) are widely
used in clinical and research applications. BCI can pro-
pose a new aim for playing videogames or interacting
with 3D virtual environments (VE). Interaction with VE
includes tasks such as navigating to modify the selec-
tion and manipulation of virtual objects.
There are several examples of VE feedback games used
in sports, puzzles, or trainings. Nowadays, many univer-
sities and laboratories are trying to provide more interac-
tions with the virtual world through the BCI. Here, we
describe some of the BCI VE feedback software.
Researchers at University College Dublin and Media
Lab Europe manufactured Mind Balance videogame
that uses BCI to interact with the virtual world. The
game was designed to move an animated character in
a 3D virtual environment. The purpose is to control the
balance of an animated character on a thin rope, based
on the EEG signals of a player.
In the other computer game, designed jointly by the
University College London and Graz University of
Technology, a disabled person in a virtual street controls
the movements of the simulated wheelchair (GRAZ-
BC). These results indicated that a disabled person sit-
ting in a wheelchair can control his/her movement in
the VE using asynchronous BCI based on signal EEG.
University of Tokyo performed several tests using a
“virtual joystick” to navigate 3-D VE. Researchers pro-
vided two virtual buttons on the left and right sides of the
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

154
VE. The participants were asked to gaze at either side to
move the camera to the other side. The detection enabled
the system to identify the button at which the user gazed.
Researchers at the University of Tokyo also worked
on a system to keep the alertness level of car drivers.
In this project, the driver’s state of concentration was
illustrated when placed in a virtual driving environment.
Accordingly, the BCI hearing system actively monitors
the state of alertness of drivers and warns them when
loss of consciousness occurs.
In the eld of promotion of neurofeedback in VE, IN-
RIA designed several BCI systems. In one of them, called
“use-the-force”, subjects were asked to control the launch
of a virtual spaceship by using real or imagined foot
movements. They studied the response of the subjects in
challenging situations (Lecuyer et al., 2008). In another
system (Gnecchi et al., 2007), neurofeedback was exam-
ined in order to diagnose ADHD and hyperactivity dis-
order. In this system, there are two graphical interfaces.
In the rst interface, when the ratio of beta/theta goes
higher than a predetermined threshold, dolphins are
moving to an area where there are sh. Having main-
tained the focus, dolphin intercepts a sh. When the
number of trapped sh increases, it reects advances in
process of treatment. In the second graphical interface,
the speed of a racing car increases when subject’s atten-
tion improved. There are various available neurofeed-
back softwares in the market whose information such
as operating systems, developers, and supported devices
could be assessed via Wikipedia (“Comparison of neu-
rofeedback software”, April 11, 2015).
8. Conclusion
In this paper, we reviewed the clinical applications of
neurofeedback, various protocols of treatment and some
of the systems designs by BCI and VR technology.
In neurofeedback, EEG is usually recorded, and vari-
ous brain-activity components are extracted and feed-
backed to subjects. During this procedure, subjects be-
come aware of the changes that occur during training
and are able to assess their progress in order to achieve
optimal performance. Electrode placement is performed
according to specic brain functions and specic symp-
toms. Considering information about these skull re-
gions, the entire treatment process is simplied. There
are several protocols in neurofeedback training, but al-
pha, beta, theta, and alpha/theta protocol are the most
commonly used ones.
BCI is an EEG-based communication device. VE is
a human-computer interface system with which users
can virtually move their viewpoint freely in real time.
The purpose of using VE is to construct a virtual envi-
ronment with natural interactivity and to create a real
sensation from multimodality. Three-dimensional VR is
much more attractive and interesting than most of two-
dimensional environments.
To date, many studies have been conducted on the
neurofeedback therapy and its effectiveness on the treat-
ment of many diseases. However, there are some meth-
odological limitations and clinical ambiguities. For ex-
ample, considering the alpha treatment protocols, there
are some issues to deal with such as how many sessions
are needed before participants can learn to exert an alert
control over their own alpha waves, or how many ses-
sions are needed before such training procedures pro-
duce the expected effect on the optimal performance,
and how long the desired effects last without feedback
(long-term effects). Thus, it is necessary to provide stan-
dard protocols to perform neurofeedback.
Similar to other treatments, neurofeedback has its own
pros and cons. Although it is a safe and non-invasive
procedure that showed improvement in the treatment
of many problems and disorders such as ADHD, anxi-
ety, depression, epilepsy, ASD, insomnia, drug addic-
tion, schizophrenia, learning disabilities, dyslexia and
dyscalculia, its validity has been questioned in terms
of conclusive scientic evidence of its effectiveness.
Moreover, it is an expensive procedure which is not
covered by many insurance companies. It is also time-
consuming and its benets are not long-lasting. Finally,
it might take several months to see the desired improve-
ments (Mauro & Cermak, 2006).
Conicts of Interest:
None declared.
References
Allen, J. J., Harmon-Jones, E., & Cavender, J. H. (2001). Ma-
nipulation of frontal EEG asymmetry through biofeedback
alters self-reported emotional responses and facial EMG.
Psychophysiology, 38(4), 685-693.
Angelakis, E., Stathopoulou, S., Frymiare, J. L., Green, D. L.,
Lubar, J. F., & Kounios, J. (2007). EEG neurofeedback: a brief
overview and an example of peak alpha frequency training
for cognitive enhancement in the elderly. Clinical Neuropsy-
chologist, 21(1), 110-129.
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

155
Basic and Clinical
April 2016. Volume 7. Number 2
Bartholdy, S., Musiat, P., Campbell, I. C., & Schmidt, U. (2013).
The potential of neurofeedback in the treatment of Eating
Disorders: A Review of the Literature. European Eating Disor-
ders Review, 21(6), 456-463.
Bauer, H., & Pllana, A. (2014). EEG-based local brain activity
feedback training-tomographic neurofeedback. Frontiers Hu-
man Neuroscience, 8, 1005. doi: 10.3389/fnhum.2014.01005.
Beatty, J., Greenberg, A., Deibler, W. P., & O’Hanlon, J. F. (1974).
Operant control of occipital theta rhythm affects performance
in a radar monitoring task. Science, 183(4127), 871-873.
Breteler, M. H., Arns, M., Peters, S., Giepmans, I., & Verho-
even, L. (2010). Improvements in spelling after QEEG-based
neurofeedback in dyslexia: a randomized controlled treat-
ment study. Applied Psychophysiol Biofeedback, 35(1), 5-11. doi:
10.1007/s10484-009-9105-2.
Budzynski, T. (2009a). Introduction to quantitative EEG and neu-
rofeedback: Advanced theory and applications (2nd ed.). Amster-
dam, Elsevier: Academic Press.
Budzynski, T. (2009b). Introduction to quantitative EEG and neu-
rofeedback: Advanced theory and applications (2nd ed.). Amster-
dam, Elsevier: Academic Press.
Christiansen, H., Reh, V., Schmidt, M. H., & Rief, W. (2014).
Slow cortical potential neurofeedback and self-management
training in outpatient care for children with ADHD: Study
protocol and rst preliminary results of a randomized
controlled trial. Frontiers Human Neuroscience, 8, 943. doi:
10.3389/fnhum.2014.00943.
Coben, R., Linden, M., & Myers, T. E. (2010). Neurofeedback
for autistic spectrum disorder: A review of the literature.
Applied Psychophysiol Biofeedback, 35(1), 83-105. doi: 10.1007/
s10484-009-9117-y.
Collura, T. F. (2000). Practical Issues Concerning EEG Biofeed-
back Devices, Protocols and Methods (Doctoral Dissertation).
Retrieved from http://openeeg.sourceforge.net/arch/att-
0944/01-part.
Collura, T. F., Guan, J., Tarrant, J., Bailey, J., & Starr, F. (2010).
EEG biofeedback case studies using live Z-score training and
a normative database. Journal of Neurotherapy, 14(1), 22-46.
Comparison of neurofeedback software. (2015, April 11). Re-
trieved from http://en.wikipedia.org/w/index.
php?title=Comparison_of_neurofeedback_software&oldid
=656032961.
Cott, A., Pavloski, R. P., & Black, A. H. (1979). Reducing epi-
leptic seizures through operant conditioning of central nerv-
ous system activity: Procedural variables. Science, 203(4375),
73-75.
Cowan, J., & Markham, L. (1994 , March). EEG biofeedback for
the attention problems of autism: A case study. In 25th the An-
nual Meeting of the Association for applied Psychophysiology and
Biofeedback.
Demos, J. N. (2005). Getting started with neurofeedback (1th ed.).
New York: W.W. Norton.
Dempster, T. (2012). An investigation into the optimum training
paradigm for alpha electroencephalographic biofeedback (PhD
Thesis). U.K.: Canterbury Christ Church University.
Dias, A. M., Van Deusen, A. M., Oda, E., & Bonm, M. R.
(2012). Clinical efcacy of a new automated hemoencepha-
lographic neurofeedback protocol. Spanish Journal of Psychol-
ogy, 15(3), 930-941.
Edmonds, W. A., & Tenenbaum, G. (2011). Case studies in ap-
plied psychophysiology: Neurofeedback and biofeedback treatments
for advances in human performance. New Jersey: John Wiley &
Sons.
Egner, T., & Gruzelier, J. H. (2001). Learned self-regulation of
EEG frequency components affects attention and event-relat-
ed brain potentials in humans. Neuroreport, 12(18), 4155-4159.
Egner, T., & Gruzelier, J. H. (2003). Ecological validity of neu-
rofeedback: modulation of slow wave EEG enhances musical
performance. Neuroreport, 14(9), 1221-1224.
Egner, T., & Gruzelier, J. H. (2004). EEG Biofeedback of low
beta band components: frequency-specic effects on vari-
ables of attention and event-related brain potentials. Clini-
cal Neurophysiology, 115(1), 131-139. doi: 10.1016/S1388-
2457(03)00353-5.
Escolano, C., Aguilar, M., & Minguez, J. (2011). EEG-based
upper alpha neurofeedback training improves working
memory performance. International Conference of the IEEE En-
gineering in Medicine and Biology Society, 2011, 2327-2330. doi:
10.1109/IEMBS.2011.6090651.
Escolano, C., Olivan, B., Lopez-del-Hoyo, Y., Garcia-Campayo,
J., & Minguez, J. (2012). Double-blind single-session neuro-
feedback training in upper-alpha for cognitive enhancement
of healthy subjects. Conference Proceedings of the IEEE Engi-
neering in Medicine and Biology Society, 2012, 4643-4647. doi:
10.1109/EMBC.2012.6347002.
Evans, J. R. (2007). Handbook of neurofeedback: dynamics and clini-
cal applications. New York: Haworth Medical Press.
Evans, J. R., & Abarbanel, A. (1999). Introduction to quantitative
EEG and neurofeedback. San Diego, Calif: Academic Press.
Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H., &
Kaiser, J. (2003). Neurofeedback treatment for attention-def-
icit/hyperactivity disorder in children: A comparison with
methylphenidate. Applied Psychophysiol Biofeedback, 28(1),
1-12.
Gevensleben, H., Holl, B., Albrecht, B., Vogel, C., Schlamp,
D., & Kratz, O., et al. (2009). Is neurofeedback an efcacious
treatment for ADHD? A randomised controlled clinical trial.
Journal of Child Psychology and Psychiatry, 50(7), 780-789. doi:
10.1111/j.1469-7610.2008.02033.
Gil, Y., Li, G., & Lee, J. (2009). Integrated real-time neurofeed-
back system to raise the frontal lobe activity: Design and Im-
plementation. Institute of Electrical and Electronics Engineers,
2009, 845-848.
Gnecchi, J. A. G., Herrera Garcia, J. C., & de Dios Ortiz Al-
varado, J. (2007, Sept). Auxiliary Neurofeedback System
for Diagnostic of Attention Decit Hyperactivity Disorder.
Proceedings of the Electronics, Robotics and Automotive Mechan-
ics Conference (pp. 135-138). Mexico: Morelos. doi: 10.1109/
CERMA.2007.4367674.
Gruzelier, J. (2009). A theory of alpha/theta neurofeedback,
creative performance enhancement, long distance functional
connectivity and psychological integration. Cognitive Process-
ing, 10(1), 101-109. doi: 10.1007/s10339-008-0248-5.
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

156
Hammer, B. U., Colbert, A. P., Brown, K. A., & Ilioi, E. C.
(2011). Neurofeedback for insomnia: a pilot study of Z-score
SMR and individualized protocols. Applied Psychophysiol Bio-
feedback, 36(4), 251-264. doi: 10.1007/s10484-011-9165-y.
Hanslmayr, S., Sauseng, P., Doppelmayr, M., Schabus, M., &
Klimesch, W. (2005). Increasing Individual Upper Alpha
Power by Neurofeedback Improves Cognitive Performance
in Human Subjects. Applied Psychophysiol Biofeedback, 30(1),
1-10. doi: 10.1007/s10484-005-2169-8.
Hardt, J. V., & Kamiya, J. (1978). Anxiety change through elec-
troencephalographic alpha feedback seen only in high anxi-
ety subjects. Science, 201(4350), 79-81.
Heinrich, H., Gevensleben, H., & Strehl, U. (2007). Annota-
tion: Neurofeedback-train your brain to train behaviour.
Journal of Child Psychology and Psychiatry, 48(1), 3-16. doi:
10.1111/j.1469-7610.2006.01665.x.
Hord, D. J., Tracy, M. L., Lubin, A., & Johnson, L. (1975). Effect
of Self‐Enhanced EEG Alpha on Performance and Mood Af-
ter Two Nights of Sleep Loss. Psychophysiology, 12(5), 585-590.
Horrell, T., El-Baz, A., Baruth, J., Tasman, A., Sokhadze, G.,
Stewart, C., & et al. (2010). Neurofeedback Effects on Evoked
and Induced EEG Gamma Band Reactivity to Drug-related
Cues in Cocaine Addiction. Journal of Neurotherapy, 14(3),
195-216. doi: 10.1080/10874208.2010.501498.
Hughes, J. R. (2008). Gamma, fast, and ultrafast waves of the
brain: Their relationships with epilepsy and behavior. Epilep-
sy & Behavior, 13(1), 25-31. doi: 10.1016/j.yebeh.2008.01.011.
Hurt, E., Arnold, L. E., & Lofthouse, N. (2014). Quantitative
EEG Neurofeedback for the Treatment of Pediatric Atten-
tion-Decit/Hyperactivity Disorder, Autism Spectrum Dis-
orders, Learning Disorders, and Epilepsy. Child and Adoles-
cent Psychiatric Clinics of North America, 23(3), 465-486.
Hynd, G. W., Lorys, A. R., Semrud-Clikeman, M., Nieves, N.,
Huettner, M. I., & Lahey, B. B. (1991). Attention decit dis-
order without hyperactivity: a distinct behavioral and neuro-
cognitive syndrome. Journal of Child Neurology, 6(1), S37-43.
Ibric, V. L., & Dragomirescu, L. G. (2009). Neurofeedback in
pain management. In T. H. Budzyknski, H. K. Budzynski, J.
R. Evans & A. Abarbanel (Eds.). Introduction to quantitative
EEG and neurofeedback: Advanced theory and applications (2nd
ed.) (pp. 417-451). Amsterdam, Elsevier: Academic Press.
Kaplan, B. J. (1975). Biofeedback in Epileptics: Equivocal Re-
lationship of Reinforced EEG Frequency to Seizure Reduc-
tion. Epilepsia, 16(3), 477-485. doi: 10.1111/j.1528-1157.1975.
tb06076.x.
Karimi, M., Haghshenas, S., & Rostami, R. (2011). Neurofeed-
back and autism spectrum: A case study. Procedia-Social
and Behavioral Sciences, 30, 1472-1475. doi: 10.1016/j.sb-
spro.2011.10.285.
Klimesch, W. (1999). EEG alpha and theta oscillations reect
cognitive and memory performance: a review and analysis.
Brain Research Reviews, 29(2–3), 169-195. doi: 10.1016/S0165-
0173(98)00056-3.
Kouijzer, M. E. J., van Schie, H. T., de Moor, J. M. H., Gerrits,
B. J. L., & Buitelaar, J. K. (2010). Neurofeedback treatment
in autism. Preliminary ndings in behavioral, cognitive, and
neurophysiological functioning. Research in Autism Spectrum
Disorders, 4(3), 386-399. doi: 10.1016/j.rasd.2009.10.007.
Kropotov, J. (2010). Introduction to Quantitative EEG, event-re-
lated potentials and neurotherapy. Amsterdam, Elsevier: Aca-
demic Press.
Kuhlman, W. N., & Allison, T. (1977). EEG feedback training
in the treatment of epilepsy: Some questions and some an-
swers. The Pavlovian Journal of Biological Science, 12(2), 112-
122. doi: 10.1007/bf03004498.
Lecuyer, A., Lotte, F., Reilly, R. B., Leeb, R., Hirose, M., &
Slater, M. (2008). Brain-Computer Interfaces, Virtual Real-
ity, and Videogames. Computer, 41(10), 66-72. doi: 10.1109/
mc.2008.410.
Leins, U., Goth, G., Hinterberger, T., Klinger, C., Rumpf, N., &
Strehl, U. (2007). Neurofeedback for Children with ADHD:
A Comparison of SCP and Theta/Beta Protocols. Applied
Psychophysiol Biofeedback, 32(2), 73-88. doi: 10.1007/s10484-
007-9031-0.
Lévesque, J., Beauregard, M., & Mensour, B. (2006a). Effect of
neurofeedback training on the neural substrates of selective
attention in children with attention-decit/hyperactivity
disorder: A functional magnetic resonance imaging study.
Neuroscience Letters, 394(3), 216-221.
Lévesque, J., Beauregard, M., & Mensour, B. (2006b). Effect of
neurofeedback training on the neural substrates of selective
attention in children with attention-decit/hyperactivity
disorder: A functional magnetic resonance imaging study.
Neuroscience Letters, 394(3), 216-221. doi: 10.1016/j.neu-
let.2005.10.100.
Linden, M., Habib, T., & Radojevic, V. (1996). A controlled
study of the effects of EEG biofeedback on cognition and be-
havior of children with attention decit disorder and learn-
ing disabilities. Biofeedback & Self Regulation, 21(3), 297. doi:
10.1007/bf02214740.
Lubar, J., & Bahler, W. W. (1976). Behavioral management of
epileptic seizures following EEG biofeedback training of the
sensorimotor rhythm. Biofeedback and Self-regulation, 1(1), 77-
104. doi: 10.1007/bf00998692.
Lubar, J. F., Shabsin, H. S., Natelson, S. E., Holder, G. S., Whitsett,
S. F., Pamplin, W. E., & et al. (1981). EEG operant conditioning
in intractable epileptics. Archives of Neurology, 38(11), 700-704.
Lubar , J. F., Swartwood, M. O., Swartwood, J. N., & O’Donnell,
P. H. (1995). Evaluation of the effectiveness of EEG neuro-
feedback training for ADHD in a clinical setting as measured
by changes in TOVA scores, behavioral ratings, and WISC-R
performance. Biofeedback and Self-regulation, 20(1), 83-99.
Lucido, M. (2012). College of social and behavioural sciences. Min-
neapolis, Minnesota: Walden University.
Markovska-Simoska, S., Pop-Jordanova, N., & Georgiev, D.
(2008). Simultaneous EEG and EMG biofeedback for peak
performance in musicians. Prilozi, 29(1), 239-252.
Martindale, C., & Armstrong, J. (1974). The relationship of cre-
ativity to cortical activation and its operant control. Journal of
Genetic Psychology, 124(2), 311-320.
Mauro, T., & Cermak, S. A. (2006). The Everything Parent’s Guide
to Sensory Integration Disorder: Get the Right Diagnosis, Under-
stand Treatments, and Advocate for Your Child. USA: Adams
Media Corporation.
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

157
Basic and Clinical
April 2016. Volume 7. Number 2
McCarthy-Jones, S. (2012). Taking back the brain: could neuro-
feedback training be effective for relieving distressing audito-
ry verbal hallucinations in patients with schizophrenia? Schiz-
ophrenia Bulletin, 38(4), 678-682. doi: 10.1093/schbul/sbs006.
Moore, N. (2000). A review of EEG biofeedback treatment of
anxiety disorders. Clinical EEG and Neuroscience (Electroen-
cephalography), 31(1), 1-6.
Moradi, A., Pouladi, F., Pishva, N., Rezaei, B., Torshabi, M., &
Mehrjerdi, Z. A. (2011). Treatment of Anxiety Disorder with
Neurofeedback: Case Study. Procedia-Social and Behavioral
Sciences, 30, 103-107. doi: j.sbspro.2011.10.021.
Orlandi, M. A., & Greco, D. (2004, Aug). A randomized, double-
blind clinical trial of EEG neurofeedback treatment for attention-
decit/hyperactivity disorder. Paper presented at the meeting
of the International Society for Neuronal Regulation, Fort
Lauderdale, FL.
Othmer, S. (2007). Progress in neurofeedback for the autism spec-
trum. Paper presented at the 38th Annual Meeting of the
Association for Applied Psychophysiology & Biofeedback.
Monterey, Canada, 15-18 February 2007.
Palsson, O. S., Pope, A. T., Ball, J. D., Turner, M. J., Nevin, S., &
deBeus, R. (2001). Neurofeedback videogame ADHD technology:
Results of the rst concept study. Paper presented at the an-
nual meeting of Association for Applied Psychophysiology
& Biofeedback, Research Triangle Park, NC.
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994).
Low resolution electromagnetic tomography: a new method
for localizing electrical activity in the brain. International Jour-
nal of Psychophysiology, 18(1), 49-65.
Perreau-Linck, E., Lessard, N., Lévesque, J., & Beauregard,
M. (2010). Effects of neurofeedback training on inhibitory
capacities in ADHD children: A single-blind, randomized,
placebo-controlled study. Journal of Neurotherapy, 14(3), 229-
242. doi: 10.1080/10874208.2010.501514.
Plotkin, W. B., & Rice, K. M. (1981). Biofeedback as a placebo:
anxiety reduction facilitated by training in either suppres-
sion or enhancement of alpha brainwaves. Journal of Consult-
ing and Clinical Psychology, 49(4), 590.
Quy, R. J., Hutt, S. J., & Forrest, S. (1979). Sensorimotor rhythm
feedback training and epilepsy: Some methodological and
conceptual issues. Biological Psychology, 9(2), 129-149. doi:
10.1016/0301-0511(79)90059-0.
Rasey, H., Lubar, J. F., McIntyre, A., Zoffuto, A., & Abbott, P. L.
(1995). EEG biofeedback for the enhancement of attentional
processing in normal college students. Journal of Neurothera-
py, 1(3), 15-21. doi: 10.1300/J184v01n03_03.
Raymond, J., Sajid, I., Parkinson, L. A., & Gruzelier, J. H. (2005).
Biofeedback and dance performance: A preliminary investi-
gation. Applied Psychophysiology Biofeedback, 30(1), 65-73.
Raymond, J., Varney, C., Parkinson, L. A., & Gruzelier, J. H.
(2005). The effects of alpha/theta neurofeedback on person-
ality and mood. Cognitive Brain Research, 23(2), 287-292.
Regestein, Q., Buckland, G., & Pegram, G. (1973). Effect of day-
time alpha rhythm maintenance on subsequent sleep. Psy-
chosomatic Medicine, 35(5), 415-418.
Ros, T., Moseley, M. J., Bloom, P. A., Benjamin, L., Parkinson, L.
A., & Gruzelier, J. H. (2009). Optimizing microsurgical skills
with EEG neurofeedback. BMC Neuroscience, 10(1), 87. doi:
10.1186/1471-2202-10-87.
Rossi-Izquierdo, M., Ernst, A., Soto-Varela, A., Santos-Pérez,
S., Faraldo-García, A., Sesar-Ignacio, Á., & Basta, D. (2013).
Vibrotactile neurofeedback balance training in patients with
Parkinson’s disease: Reducing the number of falls. Gait &
Posture, 37(2), 195-200.
Schmeidler, G., & Lewis, L. (1971). Mood changes after alpha
feedback training. Perceptual and Motor Skills, 32(3), 709-710.
Sichel, A. G., Fehmi, L. G., & Goldstein, D. M. (1995). Positive
outcome with neurofeedback treatment in a case of mild
autism. Journal of Neurotherapy, 1(1), 60-64. doi: 10.1300/
J184v01n01_08.
Staufenbiel, S., Brouwer, A. M., Keizer, A., & Van Wouwe, N.
(2014). Effect of beta and gamma neurofeedback on memory
and intelligence in the elderly. Biological Psychology, 95, 74-85.
Sterman, M. B., & Macdonald, L. R. (1978). Effects of central
cortical EEG feedback training on incidence of poorly con-
trolled seizures. Epilepsia, 19(3), 207-222.
Sterman, M. B., Macdonald, L. R., & Stone, R. K. (1974). Bio-
feedback Training of the Sensorimotor Electroencephalo-
gram Rhythm in Man: Effects on Epilepsy. Epilepsia, 15(3),
395-416. doi: 10.1111/j.1528-1157.1974.tb04016.x.
Surmeli, T., & Ertem, A. (2011). Obsessive compulsive disorder
and the efcacy of qEEG-guided neurofeedback treatment: a
case series. Clinical EEG and Neuroscience, 42(3), 195-201.
Sürmeli, T., & Ertem, A. (2007). EEG neurofeedback treatment
of patients with Down Syndrome. Journal of Neurotherapy,
11(1), 63-68.
Sürmeli, T., & Ertem, A. (2011). Obsessive Compulsive Disor-
der and the Efcacy of qEEG-Guided Neurofeedback Treat-
ment: A Case Series. Clinical EEG and Neuroscience, 42(3), 195-
201. doi: 10.1177/155005941104200310.
Surmeli, T., Ertem, A., Eralp, E., & Kos, I. H. (2012). Schizo-
phrenia and the efcacy of qEEG-guided neurofeedback
treatment: a clinical case series. Clinical EEG and Neuroscience,
43(2), 133-144. doi: 10.1177/1550059411429531.
Tan, G., Thornby, J., Hammond, D. C., Strehl, U., Canady, B.,
Arnemann, K., & Kaiser, D. A. (2009). Meta-analysis of EEG
biofeedback in treating epilepsy. Clinical EEG and Neurosci-
ence, 40(3), 173-179.
Thompson, L., Thompson, M., & Reid, A. (2010). Neurofeed-
back outcomes in clients with Asperger’s syndrome. Applied
Psychophysiol Biofeedback, 35(1), 63-81.
Thompson, M., & Thompson, L. (2003). The neurofeedback book:
An introduction to basic concepts in applied psychophysiology.
Wheat Ridge, CO: Association for Applied Psychophysiol-
ogy and Biofeedback.
Tozzo, C. A., Elfner, L. F., & May, J. G. (1988). EEG biofeedback
and relaxation training in the control of epileptic seizures.
International Journal of Psychophysiology, 6(3), 185-194.
Vernon, D., Egner, T., Cooper, N., Compton, T., Neilands, C.,
Sheri, A., & et al. (2003). The effect of training distinct neu-
rofeedback protocols on aspects of cognitive performance.
International Journal of Psychophysiology, 47(1), 75-85. doi:
10.1016/S0167-8760(02)00091-0.
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016

158
Vernon, D. J. (2005). Can neurofeedback training enhance per-
formance? An evaluation of the evidence with implications
for future research. Applied Psychophysiol Biofeedback, 30(4),
347-364. doi: 10.1007/s10484-005-8421-4.
Walker, J. E. (2010). Using QEEG-guided neurofeedback for
epilepsy versus standardized protocols: Enhanced effec-
tiveness? Applied Psychophysiol Biofeedback, 35(1), 29-30. doi:
10.1007/s10484-009-9123-0.
Walker, J. E. (2011). QEEG-guided neurofeedback for recurrent
migraine headaches. Clinical EEG and Neuroscience, 42(1), 59-61.
Wang, Q., & Sourina, O. (2013). Real-time mental arithmetic
task recognition from EEG signals. IEEE Transactions on Neu-
ral Systems and Rehabilitation Engineering, 21(2), 225-232. doi:
10.1109/TNSRE.2012.2236576.
Wenya, N., Lanshin, C., Rodrigues, J. P., Feng, W., Peng Un,
M., Pui-In, M., & et al. (2012). Neurofeedback for the treatment
of schizophrenia: Case study. Paper presented at the Virtual En-
vironments Human-Computer Interfaces and Measurement
Systems (VECIMS), 2012 IEEE International Conference on,
Tianjin, China, 2-4 July 2012.
Yan, N., Wang, J., Liu, M., Zong, L., Jiao, Y., & Yue, J., et al.
(2008). Designing a Brain-computer Interface Device for
Neurofeedback Using Virtual Environments. Medical and
Biological Engineering, 28(3), 167-172.
Zandi-Mehran, Y., Firoozabadi, M., & Rostami, R. (2014).
Improvement of Neurofeedback Therapy for Improved
Attention Through Facilitation of Brain Activity Using Lo-
cal Sinusoidal Extremely Low Frequency Magnetic Field
Exposure. Clinical EEG and Neuroscience, 46(2), 100-12. doi:
10.1177/1550059414524403.
Zoefel, B., Huster, R. J., & Herrmann, C. S. (2011). Neurofeed-
back training of the upper alpha frequency band in EEG im-
proves cognitive performance. NeuroImage, 54(2), 1427-1431.
doi: 10.1016/j.neuroimage.2010.08.078.
I Marjan Mansourian I Neurofeedback: System Design, Methodology & Clinical Applicaons
Downloaded from bcn.iums.ac.ir at 21:43 IRDT on Thursday May 12th 2016