Neurofeedback for Chronic Pain

Abstract

Neurofeedback is a novel neuromodulatory therapy where individuals are given real-time feedback regarding their brain neurophysiological signals in order to increase volitional control over their brain activity. Such biofeedback platform can be used to increase an individual’s resilience to pain as chronic pain has been associated with abnormal central processing of ascending pain signals. Neurofeedback can be provided based on electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI) recordings of an individual. Target brain rhythms commonly used in EEG neurofeedback for chronic pain include theta, alpha, beta and sensorimotor rhythms. Such training has not only been shown to improve pain in a variety of pain conditions such as central neuropathic pain, fibromyalgia, traumatic brain injury and chemotherapy induced peripheral neuropathy, but has also been shown to improve pain associated symptoms such as sleep, fatigue, depression and anxiety. Adverse events associated with neurofeedback training are often self-limited and resolve with decreased frequency of training. Provision of such training has also been explored in the home setting whereby individuals have been encouraged to practice this as and when required with promising results. Therefore, neurofeedback has the potential to provide low-cost yet holistic approach to the management of chronic pain.

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Chapter
Neurofeedback for Chronic Pain
KajalPatel, ManojSivan, JamesHenshaw and AnthonyJones
Abstract
Neurofeedback is a novel neuromodulatory therapy where individuals are given
real-time feedback regarding their brain neurophysiological signals in order to
increase volitional control over their brain activity. Such biofeedback platform can
be used to increase an individual’s resilience to pain as chronic pain has been associ-
ated with abnormal central processing of ascending pain signals. Neurofeedback
can be provided based on electroencephalogram (EEG) or functional magnetic
resonance imaging (fMRI) recordings of an individual. Target brain rhythms
commonly used in EEG neurofeedback for chronic pain include theta, alpha, beta
and sensorimotor rhythms. Such training has not only been shown to improve
pain in a variety of pain conditions such as central neuropathic pain, fibromyalgia,
traumatic brain injury and chemotherapy induced peripheral neuropathy, but
has also been shown to improve pain associated symptoms such as sleep, fatigue,
depression and anxiety. Adverse events associated with neurofeedback training
are often self-limited and resolve with decreased frequency of training. Provision
of such training has also been explored in the home setting whereby individu-
als have been encouraged to practice this as and when required with promising
results. Therefore, neurofeedback has the potential to provide low-cost yet holistic
approach to the management of chronic pain.
Keywords: neurofeedback, EEG biofeedback, fMRI biofeedback, chronic pain, pain,
fatigue, depression, anxiety, sleep
. Introduction
Neurofeedback [1, 2] is a smart biofeedback platform which provides real-time
feedback to individuals about their neurophysiological signals in order to achieve
brain activity associated with therapeutic benefit. Brain activity of an individual is
measured continuously using an EEG system during the course of neurofeedback
training and parameters describing neurophysiological signals such as alpha power
or peak alpha frequency are calculated in real-time [3]. These calculated features
of ongoing brain activity are then presented to the individual either in an audio
or visual form [3]. The idea behind this is that through repeated provision of such
feedback, the individual gains an awareness of their current brain state and can
identify different mental strategies which help them achieve the desired brain state
[4]. Once the individual identifies strategies which work for them, they can keep
practicing them over the course of multiple sessions with the final aim of being able
to implement these strategies independent of a neurofeedback session.
Neurofeedback has already been investigated extensively for the management
of several neuropsychiatric conditions [5] such as Attention Deficit Hyperactivity
Disorder (ADHD) [6], depression and anxiety [7], cognition [8] and stroke

Smart Biofeedback - Perspectives and Applications

rehabilitation [9] for example. Being able to target brain signals through neurofeed-
back can be of great benefit in conditions such as chronic pain. This is because the
perception of chronic pain depends on how multiple regions of the brain process
the ascending pain signals [10, 11]. Such central processing of incoming pain
signals has been shown to be different in chronic pain patients compared to healthy
participants by a number of studies [12–14]. Considering the brain plays such an
important in the development and maintenance of chronic pain state, being able
to target changes in the neurophysiological signal which reflect such brain activity
using a novel therapy such as neurofeedback is of great interest.
The field of neurofeedback therapy for chronic pain is rapidly developing.
Several studies have been performed on a range of medical conditions over the last
decade [15]. The current studies are highly heterogenous with a number of varia-
tions in neurofeedback protocol and delivery [15, 16]. This chapter aims to give an
overview of the neurophysiological changes observed in chronic pain and how these
have been targeted by different neurofeedback studies. We also discuss the different
aspects of neurofeedback protocols which have been used so far and the outcomes
of these studies in terms of reduction in pain and pain associated symptoms.
. Neural pathways underlying pain perception
Our understanding of the neuroscience underlying pain has evolved signifi-
cantly over time. Neural pathways involved in pain perception have been shown in
Figure . One of the earliest theories explaining pain was the “specificity theory”
(Figure : Red pathway). According to this theory, pain is experienced when an
injury to a particular part of the body leads to signals being relayed via nocicep-
tive neurons to the “pain center” [17]. The brain was considered to be a “passive
recipient of sensory information” [17].
Figure 1.
Neural pathways underlying pain perception proposed by different pain theories.

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Neurofeedback for Chronic Pain
DOI: http://dx.doi.org/10.5772/intechopen.93826
One of the landmark theories which was highly influential in changing this prior
understanding of pain was the Gate Control Theory by Melzack and Wall (1965)
[18] (Figure : Blue pathway). This theory proposed that several neurons in the
spinal cord, such as large fibers carrying touch and vibration sensations as well as
interneurons in substantia gelatinosa of the dorsal horn, modulate the incoming
signals from the site of pain, thereby influencing the final signal which is transmit-
ted to the brain for processing.
Since then, advances in neuroimaging has revealed that in addition to neu-
ral pathways in the spinal cord, several cortical structures are also involved in
modulating pain perception [19, 20] (Figure : Purple pathway). Some of the
areas which have been reported to be involved include anterior cingulate gyrus,
somatosensory cortex, insular cortex, thalamus and prefrontal cortex [19]. These
findings suggest that there is not a single “pain centre”. Instead, pain is processed
by a “pain matrix” connecting different parts of the brain, thereby, reinforcing
the idea that pain perception is a result of several sensory, affective and cognitive
processes [10, 11]. Therefore, pain experienced by an individual is an integra-
tion of the current information about the painful sensory stimulation and prior
information from previous experiences which influence the emotions, anxiety,
attention and expectations of the individual about the pain [21].
Different areas of the cortex constituting the pain matrix project onto the
hypothalamus and amygdala, which then give rise to both descending inhibitory
pathways and descending facilitatory pathways [21, 22]. These descending path-
ways directly project onto the dorsal horn of the spinal cord where gating of pain
is occurring, therefore, influence the signals which are relayed up the ascending
pathways [21, 22]. This process is known as top-down modulation of pain [23].
In summary, the pain perceived by an individual is an integration of how
different parts of the cortex process the ascending pain signals as well as how
the activity of these cortical and subcortical structures influence the ascending
pain signals via descending pathways [17, 21, 24]. With the discovery of these
higher-order processes which influence pain perception, several neuromodula-
tory therapies such as neurofeedback (NFB), hypnosis and meditation, have been
explored with the potential of controlling pain by influencing this supraspinal
cortical processing of pain [25].
. Brain rhythms associated with chronic pain
Generally, the EEG oscillations are categorized based on their frequency into
theta (4–7Hz), alpha (8–12Hz), low beta or beta1 (15–20Hz) and high beta or beta2
(22–30Hz) [26–29]. Another oscillation which is widely investigated in the field of
neuromodulation is sensorimotor rhythm (SMR). SMR refers to oscillations in the
12–15Hz range which appear in spindle-like pattern over the sensorimotor cortex
during idling of the motor cortex [30, 31]. Motor execution or motor imagery which
activates the motor cortex leads to a decrease in the SMR activity [31].
Each of these brain rhythms is associated with a specific cognitive state. For
instance, whilst alpha waves have been associated with a relaxed state, beta waves
are associated with wakefulness and a state of engagement in task. Theta waves have
been associated with drowsiness [27, 32].
Patients with chronic pain have differences in their resting-state brain (EEG)
oscillations from healthy individuals. An example of a chronic pain condition which
has been extensively investigated for identification of EEG correlates of chronic
pain has been spinal cord injury (SCI). A study by Sarnthein et al. [12] showed that
SCI patients with central neuropathic pain had increased activity of theta and beta

Smart Biofeedback - Perspectives and Applications

oscillations compared to healthy individuals. These findings were confirmed by
another study [13] which observed similar increases in theta and beta activity, but
in addition, also identified lower levels of alpha activity in this patient population.
This association between chronic pain and EEG changes was further strengthened
when Jensen et al. [33] demonstrated that even within a group of patients with
spinal cord injury, individuals with central neuropathic pain had higher theta and
lower alpha activity than patients with spinal cord injury but no chronic pain.
These patterns of EEG have also been reported in other chronic pain conditions.
For instance, patients with migraine have higher theta and delta power compared
to healthy controls [14]. Patients with fibromyalgia have been shown to have higher
theta activity with sources estimated to be in the left dorsolateral prefrontal and
orbitofrontal cortex, higher beta and gamma activity with sources estimated to be
in the insular, primary motor and primary and secondary somatosensory cortices
and slowing of the dominant alpha peak [34].
Identification of such neurophysiological correlates of chronic pain is important
as it not only provides the necessary feedback signal to increase voluntary control
in therapies such as neurofeedback, but also allows monitoring the efficacy of the
therapy in modulating the neurophysiological processes targeted by the therapy.
. Neurofeedback training protocols
There are two key modalities which have been used to provide neurofeedback –
EEG neurofeedback and fMRI neurofeedback. Whilst EEG neurofeedback provides
feedback based on the neurophysiological signals recorded through an EEG system,
fMRI neurofeedback provides feedback based on the degree of activation of a par-
ticular region of the brain detected using fMRI imaging in real time [35]. Hence it is
inevitable that there is some lag between the activation and signal detection in fMRI
neurofeedback which happens almost instantaneously in EEG neurofeedback [35].
Evidence regarding efficacy of fMRI neurofeedback in pain is limited under-
standably due to the increased difficulties and expenses associated with this form.
The common region of interest which has been targeted in fMRI studies has been
rostral anterior cingulate gyrus (rACC), whereby increased activity of rACC, mea-
sured through detecting an increase in blood oxygen level dependent signal from
the region, has been associated with pain reduction [36, 37]. However, these studies
have been severely limited in terms of number of sessions [37, 38], therefore the full
benefit of the neurofeedback which occurs over the course of several sessions has
not been explored yet in fMRI neurofeedback for chronic pain.
A number of brain rhythms have been targeted by EEG neurofeedback in order
to increase resilience to pain (Table ). The commonly targeted rhythms include
theta (4–7Hz), alpha (8–13Hz), beta (14–30Hz) and sensorimotor (12–15Hz over
the sensorimotor area) [17]. However, the change desired in each of these rhythms
varies. Whilst pain reduction has been associated with an increase in the power
of alpha and sensorimotor rhythms, contrastingly, a decrease in theta and beta
rhythms have been associated with pain relief [17]. However, very few studies target
these signals in isolation [20, 39]. More often studies target multiple signals at the
same time, whereby patients are either shown each rhythm individually at the same
time or they are shown feedback based on the ratio of two such signals [40–43].
In general, neurofeedback sessions tend to be 30–45minutes long and patients
are offered 20–40 sessions [15]. The frequency of these sessions ranges from one
to five times a week, but studies which administered more frequent sessions have
reported greater pain relief. Commonly used electrodes for providing feedback
include C3, C4, Cz, T3, T4, FP1, P3 and P4 [15].

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Neurofeedback for Chronic Pain
DOI: http://dx.doi.org/10.5772/intechopen.93826
Feedback has been provided in a range of ways. Auditory feedback has been
mainly in the form of changing volume of sound, whereby, achievement of signal
has been associated with an increase in the volume heard [44]. Visual feedback
used has been more varied (Figure ). Some studies use simple bars to show the
feedback, whereby the height of the bar is proportional to the intensity of the signal
[45]. Other studies have changed the color of the bar on achievement of signal such
that when the threshold is met, the color turns green, otherwise it remains red [43].
Some studies have tried to engage the users through the idea of games whereby the
width of a river increases as the intensity of signal increases for instance [41, 46].
Therefore, feedback has been provided in a range of ways. Another form of stimula-
tion which can be explored in the context of neurofeedback is tactile stimulation.
Some studies have even combined two forms of stimuli such as visual and auditory
whereby patient is shown an angry and shouting patient [36]. In order to calm the
patient, the individual has to achieve the desired changes in the brain rhythms.
. Efficacy of neurofeedback in management of chronic pain
Several neurofeedback studies have shown pain reduction following neuro-
feedback. Key randomized controlled trials in the field have been summarized in
Table . Reduction in pain has been reported across several pain conditions such as
Fibromyalgia [27, 29, 36, 41], Central Neuropathic Pain in Paraplegic patients
[28, 43, 47–49], Traumatic Brain Injury [39, 50], Chemotherapy-Induced Peripheral
Neuropathy [51], Primary Headache [52], Complex Regional Pain Syndrome Type I
[53], Post-Herpetic Neuralgia [37] and chronic lower back pain [54]. There is a wide
Brain rhythm Frequency Desired change
Theta 4–7Hz Decrease in power
Alpha 8–13Hz Increase in power
Beta 14–30Hz Decrease in power
Sensorimotor rhythm 12–15Hz
Over sensorimotor cortex
Increase in power
Table 1.
Neurofeedback targets [17].
Figure 2.
Schematic representation of visual stimulus provided in different neurofeedback studies.

Smart Biofeedback - Perspectives and Applications

range of pain reduction reported which can range from an average of 6–82% reduc-
tion in pain intensity [15]. A recent systematic review published showed that ten
out of twenty-one studies published in the field reported a pain reduction of greater
than 30% which is considered to be clinically significant reduction [15].
Such variability in the degree of pain reduction could be due to a number of
aspects of the neurofeedback protocol ranging from number of sessions, frequency
of sessions, target frequencies and electrodes used for feedback, for example. The
neurofeedback studies conducted so far have been highly variable on more than one
of these aspects [15, 16], making comparison of results across studies impossible.
Therefore, it is difficult to determine which of these parameters is responsible for
the difference or how to best optimize each of these aspects of the training.
Most of the neurofeedback studies have measured changes in pain immediately
following neurofeedback [39, 43, 52, 55, 56]. Furthermore, pain reduction has
been reported to be sustained even at follow up of 3–6months after completion of
neurofeedback training [28, 36, 41, 49–51, 54]. However, these studies do not report
whether the corresponding change in brain rhythm which were measured following
completion of training were also sustained at long-term follow-up. We do not know
the length of time for which the effect of neurofeedback on brain rhythms is sus-
tained. Interestingly, one study reported that although pain reduction did not occur
immediately following completion of the training course, there was improvement
in pain at follow-up [36]. This could suggest that perhaps NFB could lead to changes
in the underlying brain networks which occurs over a longer period of time but can
be sustained for longer duration. These results provide the preliminary evidence for
potential of neurofeedback for providing analgesia in chronic pain.
It has been shown that neurofeedback not only leads to reduction in pain but
leads to improvement in a number of pain associated symptoms such as depression
Study Chronic pain
condition
Target brain
oscillation
 Pain
reduction
Pain associated
symptoms reported to
improve following NFB
Goldway et al.
(2019) [36]
Fibromyalgia ↓Amygdala
activation
(fMRI)
7% REM latency
Sleep quality
Prinsloo et al.
(2018) [50]
Chemotherapy-
induced peripheral
neuropathy
↑Alpha
↓Beta
45% Fatigue
Cancer-related
symptoms
Physical functioning
Quality of life
Guan et al.
(2015) [37]
Post-herpetic
Neuralgia
↓rACC activity
(fMRI)
64% None studied
Farahani et al.
(2014) [45]
Primary headache ↑SMR
↓Theta
↓Beta
19% None studied
Caro et al.
(2011) [29]
Fibromyalgia ↑SMR
↓Theta
↓Beta
39% Fatigue
Kayiran et al.
(2010) [40]
Fibromyalgia ↑SMR
↓Theta
↓Beta
82% Fatigue
Depression
Anxiety
Social functioning
Physical functioning
Table 2.
Randomized controlled trials investigating role of neurofeedback in chronic pain conditions.

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Neurofeedback for Chronic Pain
DOI: http://dx.doi.org/10.5772/intechopen.93826
[27, 39, 41, 54, 57–60], anxiety [27, 41, 54, 57, 59], fatigue [27, 29, 41, 49, 51], and
sleep [36, 39, 49–51, 57]. These symptoms have been known to co-exist with pain in
chronic pain conditions and also known to exacerbate the individual’s pain on a day-
to-day basis [61–63]. Therefore, by being able to target these symptoms along with
pain, neurofeedback has the potential to holistically improve the well-being of these
individuals. A summary of different symptoms which have been shown to improve
following neurofeedback have been shown in Figure .
Current neurofeedback studies have a number of limitations. There are currently
only seven controlled trials in the field [29, 41, 47, 51, 52, 64, 65], of which only
one trial is of high quality [65]. Most of the trials lack appropriate blinding as the
control group are often patients on other pain medications [29, 66]. This makes the
blinding of patient difficult and could lead to patient’s belief in treatment affecting
the results. Only two studies have implemented sham neurofeedback [36, 37].
The best sham treatment to offer is debatable. One would argue that patients
could be shown the feedback signal from another region of the brain. But this
might not be best as it might be the case that another region which is used for
feedback might be the undiscovered part of the pain matrix. Another way to pro-
vide sham feedback would be to show the individual the recording from another
participant or their own recording in a reverse order. Whilst this might be a true
sham condition as the feedback shown to the individual would be independent
of the individual’s brain activity, it might mean that the patients find no relief of
symptoms and discover that it is a sham treatment. Either way, such sham neuro-
feedback needs to be implemented by more studies in order to truly understand
whether the pain reduction reported in these individuals is due to underlying
changes in neuronal networks.
Whilst we have learnt a lot about neurofeedback over the past decade, there is
still a lot which is unknown about this technique. Neurofeedback differs from other
neuromodulatory techniques such entrainment and transcranial magnetic stimula-
tion in that neurofeedback involves active involvement of the individuals in changing
Figure 3.
Schematic representation of pain and pain associated symptoms in chronic pain syndromes.

Smart Biofeedback - Perspectives and Applications

the brain oscillations, as opposed to passive reception of stimulation [5]. We do
not know which of these is a more efficient technique to alter brain oscillations yet.
Furthermore, it is also unknown what mental strategies in particular are associated
with changes in brain oscillations seen in the studies so far. Some of the common
instructions given to patients undergoing training involve asking them to stay
relaxed, imagining happy moments, revisiting happy memories and thinking about
favorite family member or friends. However, none of the studies so far document
which of these strategies actual work for the patients. Therefore, further qualitative
studies are required to see what patients have been using to actively change their
brain oscillations during neurofeedback in order to provide more focused instruc-
tions to patients undergoing training. Furthermore, studies should aim to analyze the
correlation between neurophysiological signal and pain reduction rather than solely
focusing on the behavioral outcomes [29, 41, 47, 51, 52, 64, 65]. Establishing such cor-
relation between behavioral change and changes in neurophysiological signal is key to
understanding whether the pain relief is truly due to neurofeedback.
In addition to this, there is also a possibility that once the patients have been able
to identify the mental strategy which allows them to achieve the desired brain state
and practice in the neurofeedback setting for a number a sessions, they might be
able to implement such mental strategies without the ongoing EEG signal feedback.
It is not clear if this possible or how long it might take for an individual to become
independent of the EEG feedback and still receive pain relief.
The current neurofeedback studies are highly heterogenous. It is unclear which
brain regions, oscillations, feedback form or training length is required to optimize
the improvement in pain. More studies are required comparing one aspect of the
neurofeedback training program at once in order to determine which of these
parameters provide the most therapeutic benefit.
Another area of uncertainty is the efficacy of neurofeedback in different pain
conditions. Studies so far have shown that all chronic pain condition report pain
reduction to some degree following neurofeedback. However, it is not known
whether neurofeedback is better for some chronic pain conditions than others. It
might be the case that neuronal changes seen following neurofeedback is linked to
central sensitization only, in which case several chronic pain conditions may benefit
from it equally as many pain conditions have this as the underlying pathology.
However, we do not know whether it is equally as good at treating nociceptive pain
as seen in conditions such as arthritis.
Furthermore, the role that neurofeedback will play in pain management in the
future is not clear [16]. It is not clear whether it has the true potential to substitute
pharmacological agents completely. It might be the case that it might reduce the
escalation of opioid usage in this patient cohort. Hence further studies are needed
to determine the maximum potential of this form of therapy.
. Adverse effects associated with Neurofeedback
In general, neurofeedback is well tolerated with a minority of patients experienc-
ing mild adverse events. These adverse events are often self-limiting and tend to be
controlled by decreasing the frequency of training [43, 48]. Adverse events seen
in neurofeedback studies seem to be more common in certain patient groups than
others. For instance, some individuals with spinal cord injury and central neuropathic
pain have reported some hypersensitivity of soles of the feet due to recovery of pro-
prioception or spasms of the lower limb, [28, 48]. Patients with traumatic brain injury
have reported an increase in nausea and the intensity of their headaches [39, 67]. It is
difficult to confirm that these side-effects are due to NFB as these reported symptoms


Neurofeedback for Chronic Pain
DOI: http://dx.doi.org/10.5772/intechopen.93826
are often seen in these conditions irrespective of provision of neurofeedback therapy.
Overall, NFB is safe and well-tolerated in majority of patients in most clinical studies.
. Delivery of home-based neurofeedback therapy
Neurofeedback has also been delivered in the home setting by a few recent
studies [43, 48]. This can be achieved through the use of a headset which records
activity from one single electrode, such as C4 [43, 48] or FP1 [39] and makes use of
an app on tablets to analyze and showcase feedback to the individual [28, 48]. Such
systems have been implemented in patients with central neuropathic pain [43, 48]
as well as traumatic brain injury [39]. Patients could practice neurofeedback for
5- or 10-minutes sessions as and when they wanted.
These studies have shown some promising results. With further expansion of this
technology, it might be possible for individuals to benefit from neurofeedback at their
home as and when required as patients have on average used neurofeedback 3–40
times over the course of 2–3months in these studies [43, 48]. Two of these studies have
reported around 33% reduction in pain [43, 48] whereas one of them reported 16%
reduction in pain [39] on average in participants who tried these home-based systems.
One of these studies also performed qualitative research on user experience
following such home-based systems [43]. Overall, it was reported that the patient
satisfaction score was high when measured using QUESB (Quebec User Evaluation of
Satisfaction Questionnaire). According to the patients, the key factors which affected
the frequency of their use of the home-based device were their health state, availability
of free time and their intensity of pain. Patients also put effectiveness, ease of use and
comfort as their main priority when using any such home-based device. Hence whilst
the current home-based technology used in this study showed that it could record the
data with decent quality, it also highlighted that patients wanted technology which
was able to provide neurofeedback wirelessly using headset and smart device as well as
collect information from the scalp without the use of gel to connect electrodes.
Being able to do this on a regular basis would also increase the efficacy of the
therapy and patients might be able to use neurofeedback in addition to or instead
of commonly used pharmacological agents which are associated with significant
adverse effect profiles. Therefore, home-based neurofeedback can act as a novel
treatment option to provide pain relief to patients with much fewer side effects than
current pharmacological agents [68].
. Conclusions
Neurofeedback is a newly emerging technique which can be used to achieve brain
states associated with increased resilience to pain. The results so far have been very
promising not only in terms of improvement in chronic pain, where as many as half of
the studies in the field have shown clinically significant reduction in clinical pain fol-
lowing neurofeedback, but also in terms of improvement in pain associated symptoms
such as fatigue, depression, anxiety and sleep which have also been reported to improve
with neurofeedback. Being able to target all of these co-morbidities holistically using
neurofeedback is key for the overall improvement in the well-being of chronic pain
patients because these factors are often interlinked and aggravate each other.
There is still a lot of work that needs to be done. Different aspects of training
protocols, such as target signal, number of sessions, length of sessions and scalp
region of interest, need to be optimized in order to identify parameters which lead
not only to successful modulation of the brain activity but also a corresponding

Smart Biofeedback - Perspectives and Applications

change in pain signals. Currently, it is not clear what neurofeedback protocol brings
about maximum pain relief for patients.
Furthermore, identification of mental strategies which enable individuals to
reach therapeutic brain states is also required, with the aim being that eventually
individuals will be able to practice these strategies independent of the feedback
system after an initial course of training sessions. Whilst, there is a lot of work
to do, the results so far have been promising, opening window of opportunity to
manage a number of chronic pain conditions at low cost and without the side effects
associated with the currently available pharmacological agents.
Conflict of interest
The authors have no conflict of interest to declare.
Author details
KajalPatel1*, ManojSivan2,3, JamesHenshaw2 and AnthonyJones2
1 School of Medicine, University of Manchester, Manchester, UK
2 The Human Pain Research Group, Division of Neuroscience and Experimental
Psychology, University of Manchester, Manchester, UK
3 Academic Department of Rehabilitation Medicine, University of Leeds, Leeds, UK
*Address all correspondence to: kj.patel1020@gmail.com
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms
of the Creative Commons Attribution License (http://creativecommons.org/licenses/
by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.


Neurofeedback for Chronic Pain
DOI: http://dx.doi.org/10.5772/intechopen.93826
References
[1] Evans JR. Handbook of
neurofeedback: dynamics and clinical
applications. CRC Press. 2007;
[2] Marzbani H, Marateb HR,
Mansourian M. Methodological note:
Neurofeedback: A comprehensive
review on system design, methodology
and clinical applications. Basic Clin
Neurosci. 2016 Mar 1;7(2):143-58.
[3] Bagdasaryan J, Le Van
Quyen M. Experiencing your brain:
Neurofeedback as a new bridge between
neuroscience and phenomenology.
Front Hum Neurosci. 2013;7(OCT):1-10.
[4] Alkoby O, Abu-Rmileh A, Shriki O,
Todder D. Can We Predict Who Will
Respond to Neurofeedback? A Review
of the Inefficacy Problem and Existing
Predictors for Successful EEG
Neurofeedback Learning. Vol. 378,
Neuroscience. Elsevier Ltd; 2018. p.
155-64.
[5] Budzynski TH, Budzynski HK,
Evans JR, Abarbanel A. Introduction to
quantitative EEG and neurofeedback:
Advanced theory and applications. Acad
Press. 2009;
[6] Van Doren J, Arns M, Heinrich H,
Vollebregt MA, Strehl U, K. Loo S.
Sustained effects of neurofeedback
in ADHD: a systematic review and
meta-analysis. European Child and
Adolescent Psychiatry. 2019.
[7] Schoenberg PLA, David AS.
Biofeedback for psychiatric disorders: A
systematic review. Appl Psychophysiol
Biofeedback. 2014;39(2):109-35.
[8] Gruzelier JH. EEG-neurofeedback
for optimising performance. I: a review
of cognitive and affective outcome in
healthy participants. Neuroscience and
Biobehavioral Reviews. 2014. p. 124-41.
[9] Carvalho R, Dias N, Cerqueira JJ.
Brain-machine interface of upper
limb recovery in stroke patients
rehabilitation: A systematic review.
Physiotherapy Research International.
2019.
[10] Katz WA, Rothenberg R. The
nature of pain: Pathophysiology. J Clin
Rheumatol. 2005;11(2):11-5.
[11] Reddan MC, Wager TD, M.C.
R. Brain systems at the intersection
of chronic pain and self-regulation.
Neurosci Lett [Internet]. 2019;702:24-
33. Available from: http://www.elsevier.
com/locate/neulet
[12] Sarnthein J, Stern J, Aufenberg C,
Rousson V, Jeanmonod D. Increased
EEG power and slowed dominant
frequency in patients with neurogenic
pain. Brain. 2006;129(1):55-64.
[13] Boord P, Siddall PJ, Tran Y,
Herbert D, Middleton J, Craig A.
Electroencephalographic slowing and
reduced reactivity in neuropathic pain
following spinal cord injury. Spinal
Cord. 2008;
[14] Dos Santos Pinheiro ES, De
Queirós FC, Montoya P, Santos CL,
Do Nascimento MA, Ito CH, et al.
Electroencephalographic patterns in
chronic pain: A systematic review of the
literature. PLoS One. 2016;11(2):1-26.
[15] Patel K, Sutherland H, Henshaw J,
Taylor JR, Brown CA, Casson AJ,
et al. Effects of neurofeedback in
the management of chronic pain: A
systematic review and meta-analysis
of clinical trials. Eur J Pain (United
Kingdom) [Internet]. 2020 Jun 5 [cited
2020 Jun 8];24:1440-1457. Available
from: https://onlinelibrary.wiley.com/
doi/abs/10.1002/ejp.1612
[16] Roy R, de la Vega R, Jensen MP,
Miró J. Neurofeedback for Pain
Management: A Systematic Review.
Frontiers in Neuroscience. 2020.

Smart Biofeedback - Perspectives and Applications

[17] Jensen MP, Sherlin LH, Hakimian S,
Fregni F. Neuromodulatory approaches
for chronic pain management: Research
findings and clinical implications. J
Neurother. 2009;13(4):196-213.
[18] Melzack R, Wall PD. Pain
Mechanisms: A New Theory. Science
(80- ) [Internet]. 1965 [cited 2019 Sep
29];150:971-8. Available from: https://
science-sciencemag-org.manchester.
idm.oclc.org/content/sci/150/3699/971.
full.pdf
[19] Apkarian AV, Bushnell MC,
Treede R-D, Zubieta J-K. Human brain
mechanisms of pain perception and
regulation in health and disease. Eur J
Pain [Internet]. 2005 [cited 2019 Sep
29];9:463-84. Available from: www.
EuropeanJournalPain.com
[20] Mayaud L, Wu H, Barthélemy Q,
Favennec P, Delpierre Y, Congedo M, et
al. Alpha-phase synchrony EEG training
for multi-resistant chronic low back
pain patients: an open-label pilot study.
Eur Spine J. 2019;
[21] Ossipov MH, Dussor GO, Porreca F.
Central modulation of pain. J Clin Invest
[Internet]. 2010 [cited 2019 Sep
30];120(11):3779-87. Available from:
http://www.jci.org
[22] DeLeo JA. Basic science of pain. J
Bone Jt Surg. 2006;88(2):58-62.
[23] Donaldson LF, Lumb BM. Top-
down control of pain [Internet]. Vol.
595, Journal of Physiology. 2017 [cited
2020 Jan 12]. p. 4139-40. Available from:
https://www.ncbi.nlm.nih.gov/pmc/
articles/PMC5491874/
[24] Apkarian A V, Hashmi JA,
Baliki MN. Pain and the brain:
Specificity and plasticity of the brain in
clinical chronic pain. [cited 2019 Nov 9];
Available from: http://www.iasp-pain.
org/
[25] Jensen MP, Day MA, Miro J.
Neuromodulatory treatments
for chronic pain: efficacy and
mechanisms. Nat Rev Neurol [Internet].
2014;10(3):167-78. Available from:
http://ovidsp.ovid.com/ovidweb.cgi?T=
JS&PAGE=reference&D=med10&NEW
S=N&AN=24535464
[26] Kropotov JD. Rhythms of
the Healthy Brain. In: Functional
Neuromarkers for Psychiatry
[Internet]. 2016 [cited 2020 Jan
10]. p. 403-5. Available from:
http://dx.doi.org/10.1016/
B978-0-12-410513-3.00038-3
[27] Kayiran S, Dursun E, Ermutlu N,
Dursun N, Karamursel S. Neurofeedback
in fibromyalgia syndrome. Agri.
2007;19(3):47-52.
[28] Hassan MA, Fraser M, Conway BA,
Allan DB, Vuckovic A. The mechanism
of neurofeedback training for treatment
of central neuropathic pain in
paraplegia: A pilot study. BMC Neurol.
2015;15(1).
[29] Caro XJ, Winter EF. EEG
biofeedback treatment improves certain
attention and somatic symptoms in
fibromyalgia: A pilot study. Appl
Psychophysiol Biofeedback [Internet].
2011 Sep [cited 2019 Oct 22];36(3):193-
200. Available from: http://ovidsp.
ovid.com/ovidweb.cgi?T=JS&PAGE
=reference&D=emed12&NEWS=N
&AN=51466220
[30] Collura TF, Siever D. Audio-Visual
Entrainment in Relation to Mental
Health and EEG. In: Introduction to
Quantitative EEG and Neurofeedback.
2009.
[31] Timmers D. Treating Attention
Deficits and Impulse Control. In:
Clinical Neurotherapy: Application of
Techniques for Treatment. 2013.
[32] Jensen MP, Hakimian S, Sherlin LH,
Fregni F, M.P. J, S. H, et al. New Insights
Into Neuromodulatory Approaches for
the Treatment of Pain. J Pain [Internet].


Neurofeedback for Chronic Pain
DOI: http://dx.doi.org/10.5772/intechopen.93826
2008 [cited 2019 Sep 29];9(3):193-9.
Available from: http://ovidsp.ovid.com/
ovidweb.cgi?T=JS&PAGE=reference&D
=emed10&NEWS=N&AN=50020976
[33] Jensen MP, Sherlin LH, Gertz KJ,
Braden AL, Kupper AE, Gianas A, et al.
Brain EEG activity correlates of chronic
pain in persons with spinal cord injury:
Clinical implications. Spinal Cord.
2013;51(1):55-8.
[34] Lim M, Kim JS, Kim DJ,
Chung CK. Increased low-and high-
frequency oscillatory activity in the
prefrontal cortex of fibromyalgia
patients. Front Hum Neurosci.
2016;10(MAR2016):1-11.
[35] Hawkinson JE, Ross AJ,
Parthasarathy S, Scott DJ, Laramee EA,
Posecion LJ, et al. Quantification
of adverse events associated with
functional MRI scanning and with real-
time fMRI-based training. Int J Behav
Med [Internet]. 2012;19(3):372-81.
Available from: http://ovidsp.ovid.com/
ovidweb.cgi?T=JS&PAGE=reference&D
=med8&NEWS=N&AN=21633905
[36] Goldway N, Ablin J, Lubin O,
Zamir Y, Keynan JN, Or-Borichev A, et
al. Volitional limbic neuromodulation
exerts a beneficial clinical effect on
Fibromyalgia. Neuroimage [Internet].
2019;186:758-70. Available from: http://
ovidsp.ovid.com/ovidweb.cgi?T=JS&P
AGE=reference&D=medl&NEWS=N
&AN=30408596
[37] Guan M, Ma L, Li L, Yan B,
Zhao L, Tong L, et al. Self-regulation
of brain activity in patients with
postherpetic neuralgia: a double-blind
randomized study using real-time FMRI
neurofeedback. PLoS One [Internet].
2015 Apr;10(4):e0123675. Available
from: http://www.plosone.org/article/
fetchObject.action?uri=info:doi/10.1371/
journal.pone.0123675&representation
=PDF
[38] Goldway N, Ablin J, Lubin O,
Zamir Y, Jacob Keynan N,
Or-Borichev A, et al. Beyond pain
in fibromyalgia: Limbic related eeg-
neurofeedback training improves sleep
and affect. Biol Psychiatry [Internet].
2018 May;83(9 Supplement 1):S447.
Available from: http://ovidsp.ovid.com/
ovidweb.cgi?T=JS&PAGE=reference&D
=emexa&NEWS=N&AN=621901919
[39] Elbogen EB, Alsobrooks A, Battles S,
Molloy K, Dennis PA, Beckham JC,
et al. Mobile Neurofeedback for Pain
Management in Veterans with TBI
and PTSD. Pain Med [Internet].
2019;0(0):1-9. Available from: https://
academic.oup.com/painmedicine/
advance-article/doi/10.1093/pm/
pnz269/5614403
[40] S. P, T. K, E. B, D. D, D. N.
Neuromodulation as a treatment for
chemotherapy-induced peripheral
neuropathy (CIPN): Examining
differences between placebo and
neurofeedback. Psychosom Med
[Internet]. 2019;81(4):A63. Available
from: http://ovidsp.ovid.com/ovidweb.
cgi?T=JS&PAGE=reference&D=emexb
&NEWS=N&AN=627783404
[41] Kayıran S, Dursun E, Dursun N,
Ermutlu N, Karamürsel S. Neurofeedback
intervention in fibromyalgia syndrome;
a randomized, controlled, rater
blind clinical trial. Ahles Alanoglu,
Anderberg, Babu, Basmajian,
Bennett, Caruso, Carville, Chiarioni,
Corapcoglu, Crider, Da Costa, Denk,
Draizar, Dursun, Dursun, Dursun,
Egner, Egner, Egner, Gentil, Grace,
Gracely, Guedj, Hidalgo, Hisli, Ho,
Howe, Intiso, Jensen, Jensen, r A,
editor. Appl Psychophysiol Biofeedback
[Internet]. 2010;35(4):293-302.
Available from: http://ovidsp.ovid.com/
ovidweb.cgi?T=JS&PAGE=reference&D
=med7&NEWS=N&AN=20614235
[42] Vuckovic A, Hasan MA, Fraser M,
Conway B, Allan DB. A Pilot Study on
Clinical and Neurological Effects of
Neurofeedback Training for Treatment
of Central Neuropathic Pain. In: Jensen,

Smart Biofeedback - Perspectives and Applications

W and Andersen, OK and Akay M,
editor. REPLACE, REPAIR, RESTORE,
RELIEVE - BRIDGING CLINICAL
AND ENGINEERING SOLUTIONS IN
NEUROREHABILITATION [Internet].
GEWERBESTRASSE 11, CHAM,
CH-6330, SWITZERLAND: SPRINGER
INT PUBLISHING AG; 2014. p. 823-31.
(Biosystems and Biorobotics; vol. 7).
Available from: http://link.springer.
com/10.1007/978-3-319-08072-7_113
[43] Al-Taleb MKH, Purcell M, Fraser M,
Petric-Gray N, Vuckovic A. Home used,
patient self-managed, brain-computer
interface for the management of central
neuropathic pain post spinal cord
injury: Usability study. J Neuroeng
Rehabil [Internet]. 2019 [cited 2019 Nov
18];16(1). Available from: https://doi.
org/10.1186/s12984-019-0588-7
[44] Abul Hassan M, Fraser M,
Conway BA, Allan DB, Vuckovic A. The
mechanism of neurofeedback training
for treatment of central neuropathic
pain in paraplegia: a pilot study. BMC
Neurol. 2015 Oct;15.
[45] Vuckovic A, Hasan MA, Fraser M,
Conway B, Allan DB. A Pilot Study on
Clinical and Neurological Effects of
Neurofeedback Training for Treatment
of Central Neuropathic Pain. In: Jensen,
W and Andersen, OK and Akay, M,
editor. REPLACE, REPAIR, RESTORE,
RELIEVE - BRIDGING CLINICAL
AND ENGINEERING SOLUTIONS IN
NEUROREHABILITATION. 2014. p.
823-31. (Biosystems and Biorobotics;
vol. 7).
[46] Farahani DM, Tavallaie SA,
Ahmadi K, Ashtiani AF. Comparison
of neurofeedback and transcutaneous
electrical nerve stimulation efficacy
on treatment of primary headaches: A
randomized controlled clinical trial.
Iran Red Crescent Med J. 2014 Aug
1;16(8).
[47] Jensen MP, Sherlin LH, Askew RL,
Fregni F, Witkop G, Gianas A, et
al. Effects of non-pharmacological
pain treatments on brain states. Clin
Neurophysiol [Internet]. 2013 Oct [cited
2019 Oct 22];124(10):2016-24. Available
from: http://ovidsp.ovid.com/ovidweb.
cgi?T=JS&PAGE=reference&D=med9&
NEWS=N&AN=23706958
[48] Vučković A, Altaleb MKH,
Fraser M, McGeady C, Purcell M.
EEG Correlates of Self-Managed
Neurofeedback Treatment of Central
Neuropathic Pain in Chronic Spinal
Cord Injury. Front Neurosci [Internet].
2019 Jul 25;13. Available from: https://
www.frontiersin.org/article/10.3389/
fnins.2019.00762/full
[49] Jensen MP, Gertz KJ, Kupper AE,
Braden AL, Howe JD, Hakimian S, et
al. Steps toward developing an EEG
biofeedback treatment for chronic
pain. Amtmann Bazanova, Boord,
Bromm, Bromm, Bromm, Cardenas,
Caro, Chen, Chen, Cook, Egner, Egner,
Ehde, Finnerup, Gannon, Gevensleben,
Gevensleben, Hammond, Hays,
Huber, Jasper, Jensen, Jensen, Jensen,
Jensen, Jensen, Kayiran, Krupp, Llinas,
Raymond, Ros, Sa B, editor. Appl
Psychophysiol Biofeedback [Internet].
2013 Jun [cited 2019 Oct 22];38(2):101-
8. Available from: http://ovidsp.ovid.
com/ovidweb.cgi?T=JS&PAGE=referenc
e&D=med9&NEWS=N&AN=23532434
[50] Hershaw JN, Hill-Pearson CA,
Arango JI, Souvignier CAR, Pazdan CRM.
Semi-Automated Neurofeedback
Therapy for Persistent Postconcussive
Symptoms in a Military Clinical Setting:
A Feasibility Study. Mil Med [Internet].
2020 Oct 11 [cited 2019 Oct 22];
Available from: http://www.ncbi.nlm.
nih.gov/pubmed/31603218
[51] Prinsloo S, Novy D, Driver L,
Lyle R, Ramondetta L, Eng C, et al. The
Long-Term Impact of Neurofeedback on
Symptom Burden and Interference in
Patients With Chronic Chemotherapy-
Induced Neuropathy: Analysis of a
Randomized Controlled Trial. J Pain


Neurofeedback for Chronic Pain
DOI: http://dx.doi.org/10.5772/intechopen.93826
Symptom Manage [Internet]. 2018
[cited 2019 Sep 29];55(5):1276-85.
Available from: http://www.elsevier.
com/locate/jpainsymman
[52] Farahani DM, Tavallaie SA,
Ahmadi K, Fathi Ashtiani A,
Sheikh M, Yahaghi E, et al. Comparison
of Neurofeedback and Transcutaneous
Electrical Nerve Stimulation Efficacy
on Treatment of Primary Headaches: A
Randomized Controlled Clinical Trial.
Iran Red Crescent Med J [Internet].
2014 Aug 5;16(7):e17799. Available
from: http://ircmj.com/53808.pdf
[53] Jensen MP, Grierson C,
Tracy-Smith V, Bacigalupi SC,
Othmer S. Neurofeedback treatment for
pain associated with complex regional
pain syndrome type I. Turk BBBBCCCC
deCharms EFHJMOOOPPQSS, editor.
J Neurother [Internet]. 2007;11(1):45-
53. Available from: http://ovidsp.
ovid.com/ovidweb.cgi?T=JS&PAG
E=reference&D=psyc5&NEWS=N
&AN=2007-13570-004
[54] Mayaud L, Wu H, Barthélemy Q,
Favennec P, Delpierre Y, Congedo M,
et al. Alpha-phase synchrony EEG
training for multi-resistant chronic
low back pain patients: an open-label
pilot study. Eur Spine J [Internet]. 2019
[cited 2019 Nov 18];28(11):2487-501.
Available from: https://doi.org/10.1007/
s00586-019-06051-9
[55] Caro XJ, Winter EF. EEG
biofeedback treatment improves certain
attention and somatic symptoms
in fibromyalgia: a pilot study. Appl
Psychophysiol Biofeedback [Internet].
2011;36(3):193-200. Available from:
http://ovidsp.ovid.com/ovidweb.cgi?T=
JS&PAGE=reference&D=med7&NEWS
=N&AN=21656150
[56] Vuckovic A, Altaleb MKH,
Fraser M, McGeady C, Purcell M.
EEG Correlates of Self-Managed
Neurofeedback Treatment of Central
Neuropathic Pain in Chronic Spinal
Cord Injury. Front Neurosci. 2019 Jul;13.
[57] Ibric VL, Dragomirescu LG.
Neurofeedback in pain management.
In: Budzynski, TH and Budzynski,
HK and Evans, JR and Abarbanel A,
editor. Introduction to Quantitative
EEG and Neurofeedback [Internet].
SARA BURGERHARTSTRAAT 25, PO
BOX 211, 1000 AE AMSTERDAM,
NETHERLANDS: Elsevier; 2009.
p. 417-51. Available from: https://
linkinghub.elsevier.com/retrieve/pii/
B9780123745347000162
[58] Koberda JL. LORETA Z-Score
Neurofeedback in Chronic Pain and
Headaches. In: JF TR and L, editor. Z
Score Neurofeedback [Internet]. 525
B STREET, SUITE 1900, SAN DIEGO,
CA 92101-4495 USA: Elsevier; 2015.
p. 115-39. Available from: https://
linkinghub.elsevier.com/retrieve/pii/
B9780128012918000066
[59] Koberda JL. Z-score LORETA
neurofeedback as a potential therapy
in depression/anxiety and cognitive
dysfunction. Alonzo Choi, Kessler,
Koberda, Koberda, Koberda, Koberda,
Pittenger, Riva-Posse, Schlaepfer,
Stevens, Thatcher, Williams A, editor.
Z score neurofeedback Clin Appl
[Internet]. 2015;93-113. Available from:
http://ovidsp.ovid.com/ovidweb.cgi?T=
JS&PAGE=reference&D=psyc12&NEW
S=N&AN=2014-45849-005
[60] Koberda JL, Koberda P,
Bienkiewicz AA, Moses A, Koberda L.
Pain management using 19-electrode
Z-score LORETA neurofeedback.
Caro Ibric, Jensen, Jensen, Kayiran,
Kayiran, Kenchadze, Koberda,
Koberda, Koberda, Moisset, Moont,
Prinsloo, Sawamoto, Sime, Stern,
Stokes, Thatcher, Thatcher, Thatcher,
Thatcher, Walker H, editor. J Neurother
[Internet]. 2013;17(3):179-90. Available
from: http://ovidsp.ovid.com/ovidweb.
cgi?T=JS&PAGE=reference&D=psyc10
&NEWS=N&AN=2013-30620-005

Smart Biofeedback - Perspectives and Applications

[61] Feingold D, Brill S, Goor-Aryeh I,
Delayahu Y, Lev-Ran S. Depression and
anxiety among chronic pain patients
receiving prescription opioids and
medical marijuana. J Affect Disord.
2017;
[62] Zis P, Daskalaki A, Bountouni I,
Sykioti P, Varrassi G, Paladini A.
Depression and chronic pain in the
elderly: Links and management
challenges. Clinical Interventions in
Aging. 2017.
[63] Bonvanie IJ, Oldehinkel AJ,
Rosmalen JGM, Janssens KAM. Sleep
problems and pain: A longitudinal
cohort study in emerging adults. Pain.
2016;
[64] N. G, J. A, O. L, Y. Z, J.N. K, A. O-B,
et al. Volitional limbic neuromodulation
exerts a beneficial clinical effect on
Fibromyalgia. Neuroimage [Internet].
2019;186:758-70. Available from: http://
ovidsp.ovid.com/ovidweb.cgi?T=JS&P
AGE=reference&D=medl&NEWS=N
&AN=30408596
[65] M. G, L. M, L. L, B. Y, L. Z, L. T,
et al. Self-regulation of brain activity in
patients with postherpetic neuralgia: A
double-blind randomized study using
real-time fMRI neurofeedback. PLoS
One [Internet]. 2015;10(4):e0123675.
Available from: http://www.
plosone.org/article/fetchObject.
action?uri=info:doi/10.1371/journal.pon
e.0123675&representation=PDF
[66] S. K, E. D, N. E, N. D.
Neurofeedback in fibromyalgia
syndrome. Agri [Internet].
2007;19(3):47-52. Available from: http://
ovidsp.ovid.com/ovidweb.cgi?T=JS&PA
GE=reference&D=emed10&NEWS=N
&AN=350246563
[67] J.N. H, C.A. H-P, J.I. A, C.A.R.
S. Semi-Automated Neurofeedback
Therapy for Persistent Postconcussive
Symptoms in a Military Clinical Setting:
A Feasibility Study. Mil Med [Internet].
2019; Available from: http://ovidsp.
ovid.com/ovidweb.cgi?T=JS&PAG
E=reference&D=emexc&NEWS=N
&AN=629566784
[68] Luctkar-Flude M, Groll D. A
Systematic Review of the Safety and
Effect of Neurofeedback on Fatigue
and Cognition. Integr Cancer Ther.
2015;14(4):318-40.