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NeuroRegulation http://www.isnr.org
75 | www.neuroregulation.org Vol. 7(2):75–83 2020 doi:10.15540/nr.7.2.75
Effect of EEG Neurofeedback Training in Patients with
Moderate–Severe Traumatic Brain Injury: A Clinical and
Electrophysiological Outcome Study
Rajnish K. Gupta1, Mohammed Afsar1, Rohit K. Yadav1, Dhaval P. Shukla2, and Jamuna
Rajeswaran1*
1Department of Clinical Psychology, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
2Department of Neurosurgery, National Institute of Mental Health and Neuro Sciences, Bengaluru, India
Abstract
Traumatic brain injury (TBI) is a leading cause of death, and its survivors with a disability are considered to be an
important global health priority. In view of a diverse range of disability and its impact on TBI survivors, the need
for effective rehabilitation modalities is on a high rise. Therefore, the present study was aimed to investigate the
efficacy of EEG neurofeedback training (EEG-NFT) in moderate–severe TBI patients on their clinical and
electrophysiological outcomes. The study was an experimental longitudinal design with a pre-post comparison.
A total of 14 TBI patients in a postinjury period between 3 months to 2 years were recruited. All participants
received twenty sessions of EEG-NFT. Baseline and post-NFT comparisons were made on postconcussion
symptoms (PCS) and electrophysiological variables. The result indicates a significant reduction in the severity of
PCS following EEG-NFT. A consistent pattern of reduced slow waves and fast waves amplitude ratios was also
noted at post-NFT, although it was not significant across all the brain regions. The present study suggests EEG-
NFT as a contributing factor in improving PCS and normalization of qEEG in TBI patients, which holds an
implication for clinical decision-making of EEG-NFT as a viable alternative to be offered to TBI patients.
Keywords: neurofeedback; traumatic brain injury; EEG; postconcussion symptoms; electrophysiology
Citation: Gupta, R. K., Afsar, M., Yadav, R. K., Shukla, D. P., & Rajeswaran, J. (2020). Effect of EEG neurofeedback training in patients with
moderate–severe traumatic brain injury: A clinical and electrophysiological outcome study. NeuroRegulation, 7(2), 75–83.
https://doi.org/10.15540/nr.7.2.75
*Address correspondence to: Dr. Jamuna Rajeswaran, 306, 3rd
floor, Dr. MVG Centre, Department of Clinical Psychology, National
Institute of Mental Health and Neuro Sciences (NIMHANS), Hosur
Road, Bengaluru-560029, India. Email: drjamunarajan@gmail.com
Copyright: © 2020. Gupta et al. This is an Open Access article
distributed under the terms of the Creative Commons Attribution
License (CC-BY).
Edited by:
Rex L. Cannon, PhD, SPESA Research Institute, Knoxville,
Tennessee, USA
Reviewed by:
Rex L. Cannon, PhD, SPESA Research Institute, Knoxville,
Tennessee, USA
Randall Lyle, PhD, Mount Mercy University, Cedar Rapids, Iowa,
USA
Introduction
Traumatic brain injury (TBI) disrupts the normal
functioning of the brain caused by a bump, blow, or
jolt to the head (Marr & Coronado, 2004). It is a
major concern worldwide, also referred to as “The
Silent Epidemic” (Rusnak, 2013; Vaishnavi, Rao, &
Fann, 2009). The global incidence of TBI is
estimated to be 69 million individuals per year
(Dewan et al., 2018). In India, it is estimated that
annually approximately 1.6 million individuals
sustain a TBI (Gururaj, 2002). The prevalence of
TBI increased by 8.4% from 1990 to 2016 and
accounts for a considerable portion of the global
injury burden (GBD 2016 TBI and SCI Collaborators,
2019). The major etiological factors of TBI are road
traffic accidents (60%), falls (20–25%), and violence
(10%; Gururaj, 2002). From 2003 to 2013, in India
road accidents have increased by 5% per year while
the population increased by 1.4% per year,
suggesting a high prevalence of TBI (Singh, 2017).
TBI results in a large number of deaths or survivors
with impairments in a wide array of cognitive
domains such as executive functions (Azouvi et al.,
2016), processing speed (Fong, Chan, Ng, & Ng,
2009), response inhibition (Dimoska-Di Marco,
McDonald, Kelly, Tate, & Johnstone, 2011), memory
Gupta et al. NeuroRegulation
76 | www.neuroregulation.org Vol. 7(2):75–83 2020 doi:10.15540/nr.7.2.75
(West, Curtis, Greve, & Bianchini, 2011; Wright,
Schmitter-Edgecombe, & Woo, 2010), and social
cognition (Spikman, Timmerman, Milders, Veenstra,
& van der Naalt, 2012). These impairments ascend
to the behavioral, cognitive, emotional, and physical
changes that affect a person’s quality of life (QOL;
Langlois, Rutland-Brown, & Wald, 2006). The
cognitive functioning was found impaired in
moderate–severe TBI patients even after two years
postinjury (Schretlen & Shapiro, 2003).
Postconcussion symptoms (PCS) are the most
commonly reported sequelae of TBI, which include
headache, dizziness, fatigue, temper, sleep
disturbance, memory problems, blurred vision, poor
concentration, anxiety, and irritability (Dikmen,
Machamer, Fann, & Temkin, 2010; McLean,
Dikmen, Temkin, Wyler, & Gale, 1984; Stålnacke,
2012). A significant range of psychiatric disorders
such as depression, generalized anxiety disorder,
posttraumatic stress disorder, and agoraphobia are
found to be associated with posttraumatic injury
(Bryant et al., 2010). Population-based studies
report that patients with post-head-injury are more
liable to develop epilepsy and a binge pattern of
alcohol use (Christensen, 2012; Ferguson et al.,
2010; Horner et al., 2005).
The consequences of TBI are not only circumscribed
to these overt changes and dysfunctions but also
lead to the disruptions and alterations of brain
function, including changes in electrophysiological
patterns. These alterations have been found to be
associated with poor functional outcomes. EEG
abnormalities can be focal, multifocal, or widespread
depending upon the severity and location of the
injury (Brigo & Mecarelli, 2019; Galovic, Schmitz, &
Tettenborn, 2018). A considerable amount of
studies has been shown to correspond to
quantitative EEG (qEEG) changes after the
concussion. The most common qEEG findings of
persons with mild TBI (mTBI) are attenuated alpha
frequency in the posterior region and increased
theta activity (Arciniegas, 2011; Lewine et al., 2019;
Nuwer, Hovda, Schrader, & Vespa, 2005; Tebano et
al., 1988; Thatcher, Walker, Gerson, & Geisler,
1989). Acute disruption of cortical-thalamic
networks led to an increase in delta and theta band
and a decrease in beta band in TBI (Moeller, Tu, &
Bazil, 2011). A consequential higher theta-alpha,
theta-beta, and delta-alpha amplitude ratio and
minimized EEG coherence were also noted in mTBI
(Chen, Tao, & Chen, 2006; Modarres, Kuzma,
Kretzmer, Pack, & Lim, 2016; Moeller et al., 2011;
Watson et al., 1995). An epileptiform activity has
been observed immediately followed by a diffuse
slowing of the EEG after head injury (Walker,
Kollros, & Case, 1945).
With a diverse range of disability and its impact on
TBI survivors, new intervention modalities are being
attempted to address the TBI-related issues. One of
such modalities is EEG neurofeedback training
(EEG-NFT) that uses electrophysiological measures
of an individual to self-regulate their
psychophysiological state (Ali, Viczko, & Smart,
2020). It is a noninvasive and nonpharmacological
intervention based on the principles of operant
conditioning. EEG-NFT has shown promising
effects for ameliorating cognitive, behavioral,
emotional, and physical dysfunctions among
patients with TBI (Bennett et al., 2018; Keller, 2001;
Munivenkatappa, Rajeswaran, Indira Devi, Bennet,
& Upadhyay, 2014; Reddy, Rajeswaran, Devi, &
Kandavel, 2013; Schoenberger, Shiflett, Esty, Ochs,
& Matheis, 2001).
There are very limited to no studies being attempted
of investigating clinical and electrophysiological
changes in the moderate–severe TBI following EEG-
NFT. Therefore, the present study uses the alpha
reinforcement and theta inhibition training with the
aim to reduce theta-alpha amplitude ratio to explore
the electrophysiological alterations and the
subsequent consequences on PCS among patients
with moderate–severe TBI.
Methods and Materials
Participants
The sample comprised of 19 individuals (15 males
and 4 females) diagnosed with TBI with normal or
corrected hearing and vision in the age range of 18–
50 years (mean age = 32.47 years; SD = 7.52). All
participants with TBI had a Glasgow Coma Scale
(GCS) score 12 or less with a postinjury period
between 3 months to 2 years.
Participants with a diagnosis of mTBI (GCS: 13–15),
with extracranial injuries, having a previous history of
any comorbid neurological, psychiatric, or
neurosurgical conditions, substance dependence, or
mental retardation, and those who underwent any
form of structured psychological intervention in the
last year were excluded.
Procedure
After obtaining ethical clearance from the Institute
Ethics Committee, a written informed consent form
was sought from each participant who met inclusion
criteria. Sociodemographic and clinical details were
obtained, and baseline assessments were
Gupta et al. NeuroRegulation
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conducted using the Rivermead Postconcussion
Symptoms Questionnaire (RPQ) and a resting-state
eyes-opened EEG recording. Following the
baseline, all the participants received 20 sessions of
EEG-NFT (those who completed 80% of sessions
were also considered as completers). To examine
the posttraining effect, the same baseline
assessments were readministered immediately after
the completion of EEG-NFT.
Rivermead Postconcussion Symptoms
Questionnaire. It was used to assess the severity
of the symptoms reporting in the postinjury period. It
consists of 16 items assessing the most commonly
reported PCS. The scores ranged from 0–4 where 0
indicates the symptoms were not experienced, 1 as
the symptom was no more a problem, 2 as a mild
problem, 3 as a moderate problem, and 4 as a
severe problem. The participants were asked to rate
the degree to which they experienced the
symptoms. The total score represented the overall
severity of PCS.
EEG recording. The EEG was conducted in a dimly
lit, sound-attenuated room while the patient was
seated comfortably. The recording was performed
using SynAmps amplifiers (Compumedics
Neuroscan, Charlotte, NC) with 32 Ag/AgCl, passive
electrodes, fitted in the lycra stretch cap. Sampling
frequency was kept at 1 kHz with a notch filter at 50
Hz. For eye movement, horizontal and vertical
electrooculograms (EOG) were used bipolarly. One
electrode on each mastoid was used as a reference.
Electrodes impedance was ascertained less than 10
kΩ.
Intervention. The participants received 20 sessions
of EEG-NFT conducted three times a week
spanning the whole intervention program over a
period of 2 months. It was carried out in a quiet,
dimly lit room using a dedicated NFT system
(Atlantis system, BrainMaster Technologies, Inc.,
Bedford, OH). Each participant received alpha-theta
training (reinforcing alpha and inhibiting theta)
activity with the aim of reducing the theta-alpha
amplitude ratio. The active sites were fixed at O1
and O2 locations as per the 10–20 International
system, each reference electrode on mastoid, and
the ground electrode on the forehead. An abrasive
gel was used to clean and prepare the scalp/skin
followed by mounting the electrode using a
conductive paste. Before the procedure, the goal
and nature of the task were explained thoroughly to
the participant. The display screen was selected as
per the participants’ choice. The participants were
instructed to relax and focus on the screen. The
reward was given through visual feedback (i.e., an
increase in the score), which is displayed on the
screen. Each NFT session lasted for 40-min
duration. The training was done under the
supervision of a trained clinical neuropsychologist
(as per the norms of the rehabilitation council of
India).
Data analysis. EEG data were analyzed using
Neuroscan v4.5 (Compumedics Neuroscan,
Charlotte, NC). Finite impulse response (FIR)
bandpass filter from 0.1 to 30 Hz with a zero-phase
shift at 12 dB/octave was applied to retain all
relevant frequencies. For eye movement and other
artifacts corrections, EEG data were marked
manually, and spatial filter transformation was
performed through principal component analysis
(PCA) using singular value decomposition (SVD).
Spectral analysis was performed on artifact-free data
using 1024 data points. The signals from all the
electrode positions underwent the fast Fourier
transformation (FFT) on 500 ms epochs with a
Hanning window of 1024 Hz. The resulting
frequency spectra were divided into frequency band
of interest: delta (0.1–3.0 Hz), theta (4–7 Hz), alpha
(8–12 Hz), beta (13–30 Hz).
Further statistical analyses were carried out on
SPSS v20.0. To check the normality for all values of
interest Shapiro-Wilk test was performed (Shapiro &
Wilk, 1965). The data group that was normally
distributed a paired t-test was performed, while for
the data that violated the normality assumption, a
similar nonparametric Wilcoxon signed-rank test was
used. A statistical significance threshold was set at
p < .05.
Results
From the 19 participants with TBI who were recruited
for EEG-NFT, two participants dropped out (did not
turn up for sessions after baseline assessment or
did not complete up to 80% of the sessions). From
the remaining 17 participants, three patients could
not complete baseline and/or post-NFT
assessments.
Rivermead Postconcussion Symptoms
Questionnaire (RPQ)
The RPQ-total score which forms the severity of TBI
symptoms significantly reduced (p = .018) in post-
NFT compared to the baseline. The effect size
within-subjects also showed a medium effect (0.725)
on RPQ-T scores (Table 1; Figure 1).
Gupta et al. NeuroRegulation
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Table 1
Rivermead Postconcussion Symptoms Questionnaire total (RPQ-T) score (n = 14).
S. No.
Variable
Baseline
(Mean ± SD)
Post-NFT
(Mean ± SD)
p Value
Effect Size
Cohen’s d
1
RPQ-T
16.57 ± 10.523
10.29 ± 9.587
.018*
0.725
Note. * Significance at 0.05 level.
Figure 1. Rivermead Postconcussion Symptoms
Questionnaire total (RPQ-T) score at baseline and
post-NFT (n = 14).
EEG Neurofeedback Training (EEG-NFT)
For the EEG-NFT sessions, a ratio of an average
amplitude of theta and alpha frequency bands was
calculated at O1 and O2 locations in the first and
last session. The result indicates that the theta-
alpha ratio has reduced at both O1 (p = .665) and
O2 (p = .011) locations, although this was not
statistically significant at O1 (Table 2; Figure 2).
Table 2
Average amplitude of theta-alpha ratio at O1 and O2
locations in the first and last session (n = 14).
S.
No.
Location
First Session
(Mean ± SD)
Last Session
(Mean ± SD)
p
Value
1
O1
0.967 ± 0.265
0.94 ± 0.252
.665
2
O2
1.06 ± 0.302
0.914 ± 0.28
.011*
Note. * Significance at 0.05 level.
Figure 2. The average amplitude of theta-alpha ratio at
O1 and O2 locations in the first and last session (n = 14).
EEG Analysis
For each electrode, EEG amplitude values were
averaged across the participants. Further, these
electrodes were grouped into five different brain
regions to examine the regional differences in EEG
amplitude. An average score of the individual
electrode in that region formed the score for each
region (Figure 3).
Gupta et al. NeuroRegulation
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Figure 3. 32-electrodes were grouped into five brain
regions (frontal, central, temporal, parietal, and occipital)
as per 10–20 system.
A consistent pattern of a reduced delta-alpha, theta-
alpha, and theta-beta ratios ratio was observed
across all the brain regions in post-NFT compared to
the baseline. Although this was statistically
significant only in the temporal (p = .041) and central
(p = .038) regions for delta-alpha and in the occipital
(p = .033) for theta-alpha (Table 3; Figure 4).
Table 3
Average EEG amplitude of delta-alpha, theta-alpha,
and theta-beta ratio at baseline and post-NFT (n =
14).
S.
No.
Variable
Baseline
(Mean ± SD)
Post-NFT
(Mean ± SD)
p
Value
1
Occipital
delta-
alpha
0.283 ± 0.059
0.275 ± 0.064
.599
2
Parietal
delta-
alpha
0.267 ± 0.075
0.247 ± 0.069
.122
3
Temporal
delta-
alpha
0.313 ± 0.065
0.284 ± 0.073
.041*
4
Central
delta-
alpha
0.291 ± 0.086
0.27 ± 0.078
.038*
5
Frontal
delta-
alpha
0.363 ± 0.074
0.331 ± 0.08
.054
6
Occipital
theta-
alpha
1.319 ± 0.399
1.184 ± 0.377
.033*
7
Parietal
theta-
alpha
1.186 ± 0.281
1.17 ± 0.319
.826
8
Temporal
theta-
alpha
1.088 ± 0.452
0.97 ± 0.346
.113
9
Central
theta-
alpha
1.201 ± 0.449
1.118 ± 0.406
.136
10
Frontal
theta-
alpha
1.537 ± 0.371
1.432 ± 0.409
.111
11
Occipital
theta-
beta
2.357 ± 1.067
2.244 ± 0.665
.603
12
Parietal
theta-
beta
2.712 ± 1.265
2.597 ± 1.356
.440
13
Temporal
theta-
beta
3.005 ± 1.287
2.699 ± 1.359
.062
14
Central
theta-
beta
2.831 ± 1.388
2.726 ± 1.445
.430
15
Frontal
theta-
beta
3.141 ± 1.38
2.953 ± 1.523
.302
Note. * Significance at 0.05 level.
Gupta et al. NeuroRegulation
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Figure 4. The average EEG amplitude of (a) delta-alpha,
(b) theta-alpha, and (c) theta-beta ratios at baseline and
post-NFT (n = 14).
Discussion
The current study investigated the efficacy of EEG-
NFT in patients with moderate–severe TBI on their
clinical and electrophysiological outcomes.
Participants were assessed at baseline and post-
NFT using Rivermead Postconcussion Symptoms
Questionaire total (RPQ-T) score and EEG
amplitude.
Effectiveness of EEG-NFT on Clinical Outcome
The findings from the study indicate a significant
reduction in the severity of PCS on the RPQ-T
score. These findings are in line with previous
studies showing that EEG-NFT leads to a significant
decrease in PCS (Rajeswaran, Bennett, Thomas, &
Rajakumari, 2013; Reddy et al., 2013). A study by
Reddy et al. suggested a negative correlation of
RPQ with QOL and neuropsychological functioning
(Reddy, Rajeswaran, Devi, & Kandavel, 2017).
Therefore, the reduction of PCS on RPQ-T might
contribute to improving QOL and cognitive
functioning in patients with TBI, which is
corroborated by earlier studies (Bennett, Sampath,
Christopher, Thennarasu, & Rajeswaran, 2017;
Hoffman, Stockdale, & van Egren, 1996;
Munivenkatappa et al., 2014; Reddy, Rajeswaran,
Bhagavatula, & Kandavel, 2014).
Effectiveness of EEG-NFT on the
Electrophysiological Outcome
EEG amplitude ratio is potentially an important
indicator of cognitive ability (Trammell, MacRae,
Davis, Bergstedt, & Anderson, 2017) and constitutes
a more reliable index to monitor electrophysiological
alterations over time in TBI (Álvarez et al., 2008).
The qEEG data reported herein suggest a consistent
pattern of reduced slow waves and fast waves
(SW/FW) amplitude ratios at post-NFT. Although
significant changes were observed only for delta-
alpha in the temporal and central regions and for
theta-alpha in the occipital region.
A positive association of cognitive deterioration has
been found with an increased SW/FW ratio in
patients with moderate–severe TBI (Álvarez et al.,
2008). A study by Leon-Carrion et al. indicates a
negative correlation between delta-alpha ratio and
functional outcome in patients with head injury
(Leon-Carrion, Martin-Rodriguez, Damas-Lopez,
Barroso y Martin, & Dominguez-Morales, 2009). An
increased theta-beta ratio has been related to higher
impulsive behavior (van Dongen-Boomsma et al.,
2010) and lower response inhibition (Putman, van
Peer, Maimari, & van der Werff, 2010). Therefore, a
reduction in the SW/FW amplitude ratio might be
related to better cognitive functioning (Álvarez et al.,
2008) and could be attributed to significantly
reduced PCS observed in our study.
These qEEG changes can be suggested by
modulation in thalamo-cortical networks that refines
the intrinsic neural network, led to the normalization
of qEEG pattern in TBI following EEG-NFT
(Munivenkatappa et al., 2014). Since, SW/FW
amplitude values negatively correlate with cerebral
Gupta et al. NeuroRegulation
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blood flow and brain metabolism functioning (Coles
et al., 2004; Nagata, Tagawa, Hiroi, Shishido, &
Uemura, 1989), a reduction in SW/FW values might
be associated with a recovery of the brain
metabolism in TBI (Álvarez et al., 2008).
The findings from the EEG-NFT sessions indicate
that qEEG changes were not due to chance, as
there were progressive changes in qEEG across
NFT sessions. It is also worthwhile noticing that
electrophysiological changes in the present study
were marked 3 months to 2 years of postinjury,
suggesting that these changes were not concomitant
by the time.
To conclude, the findings suggest EEG-NFT as a
contributing factor in improving postconcussion
symptoms and normalization of qEEG in patients
with moderate–severe TBI. The present study also
holds an implication for clinical decision-making of
EEG-NFT as a viable alternative to be offered to
patients with moderate-severe TBI. The limitations
of the present study are the small sample size,
limited variables, and lack of control group.
Accounting together these limitations affect the
generalizability of the study. Therefore, future
research would require structural, functional,
biochemical, and cognitive correlates on a larger
cohort following the intervention.
Acknowledgements
The authors thank all the participants who gave
consent for this study. We also thank Mr. Deepak R.
Ullal, Senior Technician, for providing the required
technical support during the EEG recordings.
Author Disclosure
No potential conflict of interest is reported by the
authors. This study was supported by the Science
and Engineering Research Board (SERB),
Department of Science and Technology (DST),
Ministry of Science and Technology, India, and
partially supported by the Foundation for
Neurofeedback and Neuromodulation Research
(FNNR), USA.
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Received: May 23, 2020
Accepted: June 22, 2020
Published: June 27, 2020
