neuroregulation

traumatic brain injury; LORETA neurofeedback

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neuroregulation

Language Rehabilitation of Traumatic Brain Injury Patient
by LORETA Z-Score Neurofeedback: A Single-Case Study

Farnaz Faridi1
, Hayat Ameri1*, Masoud Nosratabadi2
, Seyed Majid Akhavan Hejazi3
, and
Robert W. Thatcher4

1Tarbiat Modarres University, Tehran, Iran

2Department of Psychology, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

3Head, Brain, and Spinal Cord Injuries Department, Rofeideh Hospital, University of Social Welfare and Rehabilitation
Sciences, Tehran, Iran

4Applied Neuroscience, Inc., St. Petersburg, Florida, USA

Abstract
Traumatic brain injury (TBI) creates a variety of sequelae such as aphasia that can be highly challenging for
clinicians when developing rehabilitation interventions. Therefore, the present study aimed to investigate the
effectiveness of LORETA z-score neurofeedback (LZNFB) on language performance for a 21-year-old male
suffering from aphasia following TBI. To this end, LZNFB was applied while focusing on the language network for
15 sessions. The study used an experimental design with a pre–post comparison. Baseline and posttreatment
comparisons were made on qEEG/LORETA metrics, aphasia symptoms, working memory, and attention. The
results indicated clinical improvements in language, working memory, and attention performances after 15
sessions of LZNFB. Our findings suggest that LZNFB may have the potential to aid language performance among
those with TBI.

Keywords: traumatic brain injury; LORETA neurofeedback; language; working memory; attention
Citation: Faridi, F., Ameri, H., Nosratabadi, M., Hejazi, S. M. A., & Thatcher, R. W. (2021). Language rehabilitation of traumatic brain injury
patient by LORETA z-score neurofeedback: A single-case study. NeuroRegulation, 8(2), 121–126. https://doi.org/10.15540/nr.8.2.121

*Address correspondence to: Hayat Ameri, Associate Professor
of Linguistics, Tarbiat Modarres University, Tehran, Iran. Email:
h.ameri@modares.ac.ir

Copyright: © 2021. Faridi 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:
Wesley D. Center, PhD, Liberty University, Lynchburg, Virginia,
USA
Jon A. Frederick, PhD, Lamar University, Beaumont, Texas, USA

Introduction
Traumatic brain injury (TBI) is an injury to the brain
and is typically caused by an acute injury to the
head, neck, or face (Brown et al., 2019). The wide
array of problems confronting those with TBI
includes headache, fatigue, impaired memory,
reduced attention, depression, aggression, anxiety,
sleep disturbances, and sexual dysfunction (Barth et
al., 1983). Several reports indicated that TBI can
have lifelong impacts including changes in
personality and behavior (Banks, 2007; Jackson et
al., 2002).

The consequences of TBI are not limited to those
changes but also lead to electroencephalographic
(EEG) abnormalities, which can be focal or
widespread (Brigo & Mecarelli, 2019; Galovic et al.,
2017). Some studies demonstrated quantitative EEG
(qEEG) changes in patients with TBI. For example,
the attenuated alpha frequency in the posterior
region and increased theta activity are the most
common qEEG findings of individuals with TBI
(Arciniegas, 2011; Lewine et al., 2019). Moeller et al.
(2011) reported increased delta and theta bands and
a decreased beta band in TBI due to the disruption
of the cortical-thalamic network. Higher theta-alpha,
theta-beta, and delta-alpha amplitude ratios and
reduced EEG coherence were also noted in TBI
(Modarres et al., 2017). Developing medical
treatments that ameliorate the symptoms of TBI is of
great importance, and neurofeedback (NF) is one
such method.

A review of the literature shows promise for treating
some symptoms of TBI with this modality (Gray,
2017). Ayers (1989) was the first to report positive

nr.8.2.121
effects of NF on TBI-related symptoms, finding
improvements in a number of postconcussive
symptoms experienced by patients, including
decreased energy, depression, irritability,
photophobia, attention deficit, dizziness, headache,
and short-term memory loss. The role of NF in
improving cognitive, behavioral, and physical
dysfunctions among patients with TBI has been
confirmed in previous studies (Bennett et al., 2018;
Brown et al., 2019; Gray, 2017; Gupta et al., 2020;
Hershaw et al., 2020; Kaser, 2020; Koberda,
2015a).
Although previous studies have shown that NF can
mitigate many symptoms of TBI, they have not
specifically focused on language rehabilitation by
NF. Nevertheless, language therapy produces
clinically significant improvements in functional
communication, better mood, and quality of life of
people with TBI aphasia. Accordingly, the present
study sought to evaluate the efficiency of LORETA
z-score neurofeedback (LZNFB) to rehabilitate the
language deficit in a patient with aphasia following
TBI. LZNFB is one of the recent advanced
technologies of NF that increases specificity by
targeting brain network hubs (e.g., the language
network) that are referred to as Brodmann areas.
The advantage of using the z-score in LORETA NF
is the ability to receive instant comparisons using a
reference database of healthy individual z-scores
(Thatcher, 2010). These instantaneous comparisons
make it possible to find the link between patients’
symptoms and the pertinent Brodmann areas
(Thatcher, 2010).
In this study, it was hypothesized that LZNFB
intervention could potentially enhance language
performance in a patient with aphasia following TBI.
To test this hypothesis at least in a single case
investigation, 15 sessions LZNFB were applied to
the language network.
Methods
Case Description
P.F. was a 21-year-old, right-handed male who
suffered from aphasia after trauma. Ten months
prior to our assessment, he had an accident, and his
head had been hurt at the right inferior frontal area.
After being unconscious for one month following the
accident, the patient underwent surgery on his head.
Table 1 presents the demographic information of the
patient when he was hospitalized following the
trauma. At the time of the assessment, he was alert
and oriented and could follow commands, although
his language performance was poor.
Table 1
Demographic Information of P.F When Hospitalized After
Trauma
Severity PTA Age LOC GCS
7 277 21 30 6
Note. The severity index is a number between 1 and 10,
indicating the severity of TBI based on discriminant
classification. Values in the range of 1 to 3, 3 to 5, and > 5
indicate mild, moderate, and severe head trauma,
respectively. PTA: Posttraumatic amnesia; LOC: Loss of
consciousness; GCS: Glasgow Coma Scale.
Intervention
Power spectral analyses were performed on 5-min
segments of the eyes-closed resting state. An EEG
was recorded from 19 scalp locations based on the
international 10–20 system of electrode placement
using the linked ear as a reference. Using a
Medicom amplifier and EEG Studio Acquisition
software, qEEG data were collected. In addition,
editing and digital analysis of the qEEG data were
conducted using NeuroGuide software and a
comparative database. The protocol included
LZNFB to focus on the language network in the
symptom checklist, which was developed with the
goal of linking symptoms to the areas of the brain.
Brodmann areas (BA) in this language network
include 22, 39, 40, 41, 42, 44, and 45. Learning
reinforcement in neurofeedback was provided using
television shows or animations that increased in size
when meeting the difficulty thresholds.
The qEEG/LORETA analysis was completed by
NEUROSTAT and NeuroGuide software. The
available neurocognitive testing batteries (Persian
aphasia battery, Stroop test, digit span, and
word/nonword test) were used before and after
LZNFB and compared using the Barlow formula. The
formula for recovery percentage is as follows:
∆𝐴 =
𝐴2− 𝐴1
𝐴2
(100)

As suggested by Barlow et al. (2007), if the results
are greater than 15%, we can conclude that the
results are clinically significant and treatment is
successful.
Results
The pretreatment qEEG demonstrated elevated
levels of all brain waves except alpha in the frontal
and temporal regions. After 20 LZNFB sessions,
brain wave amplitudes were closer to values from
Faridi et al. NeuroRegulation
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the database, as reflected by reduced z-scores
(Figure 1).
The percentage difference between the baseline and
last session of treatment was computed, revealing
that the largest changes were found in delta waves
at F7 and in high beta waves at F8, T4, T5, C3, and
F7 (Table 2).
Our neuropsychological assessments also indicated
improvements in the posttreatment score as
compared to baseline (Table 3).
Figure 1. Surface Maps of the Z-score Distribution (Full EEG).
Note. The qEEG map shows the magnitude of deviations from the normal database using colors. The z-score = 0 is defined
as normal (green color). Scores less or more than the normal database are displayed by blue and red colors, respectively.
EEG: Electroencephalography; 1 = Baseline qEEG; 2 = After 15 LZNFB sessions qEEG.
Table 2
The Largest Differences Between Baseline and Posttreatment
Location F8 T4 F7 T5 C3 F7
Brain wave HB HB delta HB HB HB
Percentage
change
89% 88% 86% 84% 84% 81%
Note. F = Frontal; T = Temporal; C = Central; HB = High beta.
Table 3
Neuropsychological Test Scores Before and After LZNFB
Language test Language Pretreatment Posttreatment ΔA (%)
Speed of speech 32.9 53.7 38.7%
Lexical richness 0.79 0.96 17.7%
Utterance 11 14 21.4%
Fluency 6 7 14.2%
Total word number 39 52 25%
Faridi et al. NeuroRegulation
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Table 3
Neuropsychological Test Scores Before and After LZNFB
Working memory test Digit span 6 10 40%
Word span 6 8 25%
Nonword span 4 4 0
Stroop test Correct answers
(congruent)
28 48 39%
Correct answer
(incongruent)
21 46 54%
Note. LZNFB: LORETA Z-score neurofeedback. Clinically significant differences are shown in red (ΔA% > 15% is clinically
significant).
Discussion
This study aimed to analyze the efficacy of LZNFB
intervention for the treatment of aphasia following
TBI. A qEEG-guided LZNFB protocol was designed
for this purpose. Previous studies of TBI
rehabilitation by NF have not focused on language
performance. This study specifically evaluated the
efficacy of LZNFB to rehabilitate the language deficit
in a TBI patient. To this end, changes in
qEEG/LORETA and aphasia battery metrics after 15
sessions of LZNFB were analyzed, as were changes
in working memory and attention scores from pre- to
posttreatment. The results showed that fifteen 40-
min NF sessions brought the EEG metrics within
normal ranges and were effective in improving
aphasia symptoms and cognitive performance. The
findings of the current case study can be regarded
as a promising addition to the treatment planning for
TBI-related language problems in the future.
Our findings are consistent with those of previous
studies regarding the effectiveness of NF on
mitigating TBI symptoms (Bennett et al., 2018; Gray,
2017; Gupta et al., 2020; Kaser, 2020; Rostami et
al., 2017).
Effectiveness of LZNFB on the
Electrophysiological Outcome
At baseline qEEG demonstrated increased delta,
theta, and beta bands at frontal and temporal
locations, as well as decreased alpha at the
posterior area. Increased delta and decreased alpha
bands are known to be directly correlated with
cortical metabolism (Szelies et al., 1999). The
decreased alpha band at the posterior region and
increased theta found in our study have also been
seen in other studies (Arciniegas, 2011; Lewine et
al., 2019). The increased delta and theta in our
study are in line with those of the study of Moeller et
al. (2011) and might be due to the disruption of the
cortical-thalamic network in TBI. While increased
beta occurred in this instance of TBI, it was not

found in some similar studies (Leon-Carrion et al.,
2008; Tebano et al., 1988). However, some other
studies also found increased beta in TBI subjects
(Randolph & Miller, 1988; Thornton, 2003), with the
researchers concluding that the increased beta was
consistently a negative predictor of cognitive
performance.
After 20 LZNFB sessions, the qEEG map showed an
overall improvement (Figure 1). Our finding of
neurological recovery by LZNFB is supported by
previous studies that have confirmed its
effectiveness in areas such as cerebrovascular
accident rehabilitation (Koberda & StodolskaKoberda, 2014), depression/anxiety and cognitive
dysfunction (Koberda, 2015b), addiction (Faridi et
al., 2020), attention-deficit/hyperactivity disorder
(Koberda et al., 2014), pain management (Koberda
et al., 2013), seizure (Koberda & Frey, 2015), and
TBI (Koberda, 2015a).
Based on the qEEG analysis, the largest differences
between baseline and posttreatment were
associated with the F8, F7, T5, and C3 locations
(Table 2). The F8 and F7 electrodes correspond to
BA 47, which is part of Broca’s area and associated
with the processing of syntax in oral and sign
languages, musical syntax, and semantic aspects of
language (Ardila, 2014). The T4 and T5 electrodes
correspond to BA 22, which is located at the
superior temporal gyrus and is part of Wernicke’s
area which is involved in speech comprehension.
Further, the C3 electrode corresponds to BA 2,
which is located in the primary somatosensory
cortex, and the main function of this area is the
cognitive control of language (Mofrad et al., 2020).
Effectiveness of LZNFB on the Clinical Outcome
Our assessment of the aphasia battery showed that
P.F. had clinically significant recovery following
treatment with LZNFB (Table 3). The clinical
recovery of working memory and attention were also
evident (Table 3). Several studies have reported the
Faridi et al. NeuroRegulation
125 | www.neuroregulation.org Vol. 8(2):121–126 2021 doi:10.15540/nr.8.2.121
relationship between language and working memory
(Emmorey et al., 2017; Fitz et al., 2020), as well as
language and attention (Galassi et al., 2020; Peach
et al., 2017; Vig et al., 2020; Villard & Kiran, 2017;
Wang et al., 2019), probably indicating that
language is not independent of other cognitive
performances; in other words, there is mutual
interaction in this regard.
Limitations
This study had some limitations, including the
sample size, which was a single case without a
control group. Future studies would benefit from a
larger sample size to maximize the power and
accuracy of their results. In addition, exploring the
relationship between TBI severity and LZNFB
training effects may be a beneficial focus in the
future.
Conclusion
The present preliminary findings suggest that LZNFB
may have the potential to aid language performance
among those with TBI. It was also found that the
rehabilitation of the language network may improve
working memory and attention in TBI cases. The
result of this case highlights the need for
investigating the efficacy of LZNFB not only as a
treatment for aphasia but also as a tool for improving
cognitive performance more generally.
Author Declaration
The authors declare that they have no grants,
financial interests, or conflicts of interest to disclose.
References
Arciniegas, D. B. (2011). Clinical electrophysiologic assessments
and mild traumatic brain injury: State-of-the-science and
implications for clinical practice. International Journal of
Psychophysiology, 82(1), 41–52. https://doi.org/10.1016
/j.ijpsycho.2011.03.004
Ardila, A. (2014). Aphasia handbook. Miami, FL: Florida
International University.
Ayers, M. E. (1989, June). Electroencephalographic
neurofeedback and closed head-injury of 250 individuals.
Biofeedback and Self-regulation, 14(2), 130.
Banks, M. E. (2007). Overlooked but critical: Traumatic brain
injury as a consequence of interpersonal violence. Trauma,
Violence, & Abuse, 8(3), 290–298. https://doi.org/10.1177
/1524838007303503
Barlow, D. Andrasik, F., & Hersen, M. (2007). Single Case

Experimental Designs: Strategies for Studying Behavior
Change. London, UK: Allyn & Bacon.
Barth, J. T., Macciocchi, S. N., Giordani, B., Rimel, R., Jane, J. A.,
& Boll, T. J. (1983). Neuropsychological sequelae of minor
head injury. Neurosurgery, 13(5), 529–533. https://doi.org
/10.1227/00006123-198311000-00008
Bennett, C. N., Gupta, R. K., Prabhakar, P., Christopher, R.,
Sampath, S., Thennarasu, K., & Rajeswaran, J. (2018).
Clinical and biochemical outcomes following EEG
neurofeedback training in traumatic brain injury in the context
of spontaneous recovery. Clinical EEG and Neuroscience,
49(6), 433–440. https://doi.org/10.1177/1550059417744899
Brigo, F., & Mecarelli, O. (2019). Traumatic brain injury. In O.
Mecarelli (Ed.), Clinical electroencephalography (pp. 617–
622). Switzerland: Springer International Publishing.
Brown, J., Clark, D., & Pooley, A. E. (2019). Exploring the use of
neurofeedback therapy in mitigating symptoms of traumatic
brain injury in survivors of intimate partner violence. Journal
of Aggression, Maltreatment & Trauma, 28(6), 764–783.
https://doi.org/10.1080/10926771.2019.1603176
Emmorey, K., Giezen, M. R., Petrich, J. A. F., Spurgeon, E., &
Farnady, L. O. G. (2017). The relation between working
memory and language comprehension in signers and
speakers. Acta Psychologica, 177, 69–77. https://doi.org
/10.1016/j.actpsy.2017.04.014
Faridi, A., Taremian, F., Thatcher, R. W., Dadashi, M., & Moloodi,
R. (2020). Comparison of LORETA z score neurofeedback
and cognitive rehabilitation in terms of their effectiveness in
reducing craving in opioid addicts [Manuscript accepted for
publication]. Basic and Clinical Neuroscience. https://doi.org
/10.32598/bcn.2021.1946.1
Fitz, H., Uhlmann, M., Van den Broek, D., Duarte, R., Hagoort, P.,
& Petersson, K. M. (2020). Neuronal spike-rate adaptation
supports working memory in language processing.
Proceedings of the National Academy of Sciences of the
Unites States of America, 117(34), 20881–20889.
https://doi.org/10.1073/pnas.2000222117
Galassi, A., Lippi, M., & Torroni, P. (2020). Attention in natural
language processing. IEEE Transactions on Neural Networks
and Learning Systems, 1–18. http://dx.doi.org/10.1109
/TNNLS.2020.3019893
Galovic, M., Schmitz, B., & Tettenbom, B. (2017). EEG in
inflammatory disorders, cerebrovascular diseases, trauma
and migraine. In D. L. Schomer, & F. H. L. da Silva (Eds.),
Niedermeyer’s electroencephalography: Basic principles,
clinical applications, and related fields (7th ed., pp. 371–412).
Oxford, UK: Oxford University Press. https://doi.org/10.1093
/med/9780190228484.003.0015
Gray, S. N. (2017). An overview of the use of neurofeedback
biofeedback for the treatment of symptoms of traumatic brain
injury in military and civilian populations. Medical
Acupuncture, 29(4), 215–219. https://doi.org/10.1089
/acu.2017.1220
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
Hershaw, J. N., Hill-Pearson, C. A., Arango, J. I., Souvignier, A.
R., & Pazdan, R. M. (2020). Semi-automated neurofeedback
therapy for persistent postconcussive symptoms in a military
clinical setting: A feasibility study. Military Medicine, 185(3–4),
e457–e465. https://doi.org/10.1093/milmed/usz335
Jackson, H., Philp, E., Nuttall, R. L., & Diller, L. (2002). Traumatic
brain injury: A hidden consequence for battered women.
Professional Psychology: Research and Practice, 33(1), 39–
45. https://doi.org/10.1037/0735-7028.33.1.39
Kaser, A. (2020). The influence of neurofeedback training on
verbal episodic memory in a TBI sample. Waco, TX: Baylor
University.
Koberda, J. L. (2015a). LORETA Z-score neurofeedbackeffectiveness in rehabilitation of patients suffering from
traumatic brain injury. Journal of Neurology and
Neurobiology, 1(4), 1–9. https://doi.org/10.16966/2379-
7150.113
Koberda, J. L. (2015b). Z-score LORETA neurofeedback as a

potential therapy in depression/anxiety and cognitive
dysfunction. In R. W. Thatcher & J. F. Lubar (Eds.), Z score
Faridi et al. NeuroRegulation
126 | www.neuroregulation.org Vol. 8(2):121–126 2021 doi:10.15540/nr.8.2.121
neurofeedback: Clinical applications (pp. 93–113). San Diego,
CA: Academic Press.
Koberda, J., & Frey, L. (2015). Z-score LORETA neurofeedback
as a potential therapy for patients with seizures and refractory
epilepsy. Journal of Neurology and Neurobiology, 1(1).
https://doi.org/10.16966/2379-7150.101
Koberda, J. L., Koberda, P., Bienkiewicz, A. A., Moses, A., &
Koberda, L. (2013). Pain management using 19-electrode Zscore LORETA neurofeedback. Journal of Neurotherapy,
17(3), 179–190. https://doi.org/10.1080
/10874208.2013.813204
Koberda, J. L., Koberda, P., Moses, A., Winslow, J., Bienkiewicz,
A., & Koberda, L. (2014). Z-score LORETA neurofeedback as
a potential therapy for ADHD. Biofeedback, 42(2), 74–81.
https://doi.org/10.5298/1081-5937-42.2.05
Koberda, J. L., & Stodolska-Koberda, U. (2014). Z-score LORETA
neurofeedback as a potential rehabilitation modality in
patients with CVA. Journal of Neurology & Stroke, 1(5),
00029. https://doi.org/10.15406/jnsk.2014.01.00029
Leon-Carrion, J., Martin-Rodriguez, J. F., Damas-Lopez, J., Y
Martin, J. M. B., & Dominguez-Morales, M. D. R. (2008). A
QEEG index of level of functional dependence for people
sustaining acquired brain injury: The Seville Independence
Index (SINDI). Brain Injury, 22(1), 61–74. https://doi.org
/10.1080/02699050701824143
Lewine, J. D., Plis, S., Ulloa, A., Williams, C., Spitz, M., Foley, J.,
Paulson, K., Davis, J., Bangera, N., Snyder, T., & Weaver, L.
(2019). Quantitative EEG biomarkers for mild traumatic brain
injury. Journal of Clinical Neurophysiology, 36(4), 298–305.
https://doi.org/10.1097/WNP.0000000000000588
Modarres, M. H., Kuzma, N. N., Kretzmer, T., Pack, A. I., & Lim,
M. M. (2017). EEG slow waves in traumatic brain injury:
Convergent findings in mouse and man. Neurobiology of
Sleep and Circadian Rhythms, 2, 59–70. https://doi.org
/10.1016/j.nbscr.2016.06.001
Moeller, J. J., Tu, B., & Bazil, C. W. (2011). Quantitative and
qualitative analysis of ambulatory electroencephalography
during mild traumatic brain injury. Archives of Neurology,
68(12), 1595–1598. https://doi.org/10.1001
/archneurol.2011.1080
Mofrad, F. T., Jahn, A., & Schiller, N. O. (2020). Dual function of
primary somatosensory cortex in cognitive control of
language: Evidence from resting state fMRI. Neuroscience,
446, 59–68. https://doi.org/10.1016
/j.neuroscience.2020.08.032
Peach, R. K., Nathan, M. R., & Beck, K. M. (2017). Languagespecific attention treatment for aphasia: Description and
preliminary findings. Seminars in Speech and Language,
38(1), 5–16. https://doi.org/10.1055/s-0036-1597260
Randolph, C., & Miller, M. H. (1988). EEG and cognitive
performance following closed head injury.
Neuropsychobiology, 20(1), 43–50. https://doi.org/10.1159
/000118471
Rostami, R., Salamati, P., Yarandi, K. K., Khoshnevisan, A.,
Saadat, S., Kamali, Z. S., Ghiasi, S., Zaryabi, A., Ghazi Mir
Saeid, S. S., Arjipour, M., Rezaee-Zavareh, M. S., & RahimiMovaghar, V. (2017). Effects of neurofeedback on the shortterm memory and continuous attention of patients with
moderate traumatic brain injury: A preliminary randomized
controlled clinical trial. Chinese Journal of Traumatology,
20(5), 278–282. https://doi.org/10.1016/j.cjtee.2016.11.007
Szelies, B., Mielke, R., Kessler, J., & Heiss, W.-D. (1999). EEG
power changes are related to regional cerebral glucose
metabolism in vascular dementia. Clinical Neurophysiology,
110(4), 615–620. https://doi.org/10.1016/S1388-
2457(98)00052-2
Tebano, M. T., Cameroni, M., Gallozzi, G., Loizzo, A., Palazzino,
G., Pezzini, G., & Ricci, G. F. (1988). EEG spectral analysis
after minor head injury in man. Electroencephalography and
Clinical Neurophysiology, 70(2), 185–189. https://doi.org
/10.1016/0013-4694(88)90118-6
Thatcher, R. W. (2010). LORETA Z score biofeedback.
NeuroConnections, 9–13.

Thornton, K. (2003). The electrophysiological effects of a brain
injury on auditory memory functioning: The QEEG correlates
of impaired memory. Archives of Clinical Neuropsychology,
18(4), 363–378. https://doi.org/10.1093/arclin/18.4.363
Vig, J., Madani, A., Varshney, L. R., Xiong, C., Socher, R., &
Rajani, N. F. (2020). BERTology meets biology: Interpreting
attention in protein language models [preprint]. ICLR 2021,
arXiv:2006.15222 [cs.CL].
Villard, S., & Kiran, S. (2017). To what extent does attention
underlie language in aphasia? Aphasiology, 31(10), 1226–
1245. https://doi.org/10.1080/02687038.2016.1242711
Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & van den Hengel,
A. (2019). Neighbourhood watch: Referring expression
comprehension via language-guided graph attention
networks. Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2019, 1960–1968.
Received: January 21, 2021
Accepted: April 24, 2021
Published: June 30, 2021

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