A Novel EEG Based Spectral Analysis of Persistent Brain Function Alteration in Athletes with Concussion History

Abstract

The neurocognitive sequelae of a sport-related concussion and its management are poorly defined. Detecting deficits are vital in making a decision about the treatment plan as it can persist one year or more following a brain injury. The reliability of traditional cognitive assessment tools is debatable, and thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle post-concussive alterations. In this study, we calculated neurocognitive deficits combining EEG analysis with three standard post-concussive assessment tools. Data were collected for all testing modalities from 21 adolescent athletes (seven concussive and fourteen healthy) in three different trials. For EEG assessment, along with linear frequency-based features, we introduced a set of time-frequency (Hjorth Parameters) and nonlinear features (approximate entropy and Hurst exponent) for the first time to explore post-concussive deficits. Besides traditional frequency-band analysis, we also presented a new individual frequency-based approach for EEG assessment. While EEG analysis exhibited significant discrepancies between the groups, none of the cognitive assessment resulted in significant deficits. Therefore, the evidence from the study highlights that our proposed EEG analysis and markers are more efficient at deciphering post-concussion residual neurocognitive deficits and thus has a potential clinical utility of proper concussion assessment and management.

Full text

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A Novel EEG Based Spectral
Analysis of Persistent Brain
Function Alteration in Athletes with
Concussion History
Tamanna T. K. Munia1, Ali Haider
1, Charles Schneider1, Mark Romanick2 & Reza Fazel-Rezai1
The neurocognitive sequelae of a sport-related concussion and its management are poorly dened.
Detecting decits are vital in making a decision about the treatment plan as it can persist one year or
more following a brain injury. The reliability of traditional cognitive assessment tools is debatable, and
thus attention has turned to assessments based on electroencephalogram (EEG) to evaluate subtle
post-concussive alterations. In this study, we calculated neurocognitive decits combining EEG analysis
with three standard post-concussive assessment tools. Data were collected for all testing modalities
from 21 adolescent athletes (seven concussive and fourteen healthy) in three dierent trials. For EEG
assessment, along with linear frequency-based features, we introduced a set of time-frequency (Hjorth
Parameters) and nonlinear features (approximate entropy and Hurst exponent) for the rst time to
explore post-concussive decits. Besides traditional frequency-band analysis, we also presented a new
individual frequency-based approach for EEG assessment. While EEG analysis exhibited signicant
discrepancies between the groups, none of the cognitive assessment resulted in signicant decits.
Therefore, the evidence from the study highlights that our proposed EEG analysis and markers are more
ecient at deciphering post-concussion residual neurocognitive decits and thus has a potential clinical
utility of proper concussion assessment and management.
A concussion is a complex pathophysiological procedure which is induced by sudden impulsive biomechan-
ical forces aecting the brain1. In the US alone, sport and physical activity cause nearly 4 million concussions
each year2,3. It is critical to assess concussion and mild traumatic brain injury (mTBI) with high accuracy to
avoid anxiety, sensitivity and cognitive biases which appear as post-concussion syndrome. Moreover, insucient
follow-up and treatment can put the post-concussive person at the risk of neurobiological depression with anxiety
resulting in a longer concussion recovery time. erefore, proper understanding and measuring of concussions
are essential to treat the psychological factors as a means of eective prevention which, in turn, can lead to a
rapid post-concussion recovery period. When examining performance metrics related to motor control, it is
well established that individuals diagnosed with the post-concussion syndrome can show marked impairments
in reaction times4, visual motor processing5, gait stability6, postural balance7 and dynamic gait analysis8,9. More
importantly, it is a primary concern for both amateur and professional athletes. Because the symptoms of con-
cussions sometimes go unnoticed or are self-reported and tend to subside within 1–2 weeks10, many athletes fail
to seek immediate and proper medical care. Furthermore, high school athletes tend to purposely avoid reporting
their concussions in order to prevent being “benched” during subsequent games11. ough almost all recreational
participants express their concern about post-concussion syndrome, most competitive athletes keep quiet about
their minor physical discomforts or even deny considerable pain for the sake of pursuing their career goals.
Although athletes’ willingness of accepting risks greatly varies with the competition stages, game completion
levels and types of sports, it’s more likely that many individuals will choose to continue to play with a concussion
rather than remove themselves from competition12. However, such a decision can pose a risk to their health with
the potential for repeated head trauma13. Athletes have been shown to suer from cognitive decits up to three
1Department of Electrical Engineering, University of North Dakota, Grand Forks, 58202, USA. 2Department of
Physical Therapy, University of North Dakota, Grand Forks, 58202, USA. Correspondence and requests for materials
should be addressed to R.F.-R. (email: reza@und.edu)
Received: 28 July 2017
Accepted: 21 November 2017
Published: xx xx xxxx
OPEN
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years aer their brain injury incidents, exhibiting lower performance on select neuropsychological tasks when
compared to an age-matched non-concussed group14.
Evidently, the challenges in concussion assessment have led to the studies exploiting the sensitivity of EEG
spectral features to mild, moderate, and severe traumatic brain injury over the time span as short as 15 days to
four years post-concussion.
Researchers have accomplished the quantitative analysis of the EEG signals collected from the concussed sub-
ject to evaluate the post-concussion physical and clinical recovery. Additional studies suggest that the EEG spec-
tral prole varies with acute mTBI due to the change in the cognitive state during the resting stage15,16. In essence,
the spectral prole of EEG is also altered in acute mTBI and during any anomaly of consciousness. However,
researchers argue whether mTBI can evoke long-term variations in spectral information. Also, identication of
any long-term change is sometimes controversially attributed to psychiatric comorbidity such as posttraumatic
stress disorder (PTSD). So far, long-term neurological changes have remained indistinct. Nevertheless, many
ndings support that brain volume, and white matter can be aected by mTBI17. Likewise, the resting state activa-
tion stage can be sensitive to mTBI. Another study found that EEG measurement was able to predict the return to
play better than other measurement types18. Notably, one study examined EEG and showed that frequency infor-
mation changes for as long as six months aer the mTBI occurrence19. All these ndings underscore the fact that
the power of each frequency component of EEG can reveal signicant physiological and clinical ndings. ough
there is a necessity to examine the details of spectral patterns aer a mTBI incident, only a relatively small number
of studies compared the spectral proles just with a group of frequencies bounded by specic bands.
e goal of the current research is to look into the spectral proles as a potential measurement tool which
can expose the long-term cognitive impairment aer an analytical study of EEG signals. To test our hypothesis,
we utilized visual (King-Devick (K-D) Test), postural (Balance Error Scoring System (BESS)) and neurological
(Immediate Post-Concussion Assessment and Cognitive Testing battery (ImPACT)) tests, along with a novel
EEG spectral analysis that computes the distinguishing features from each individual component of EEG, as
well as from the set of conventional frequency bands. We also utilized novel time and nonlinear feature-based
analysis to evaluate the EEG of injured and healthy athletes that provide unique and complementary measures
of post-concussion deciencies. Herein, we report that though postural, visual and neurological tests were una-
ble to detect the decits associated with a long-term concussion history, the EEG linear and nonlinear feature
based spectral analysis, both in terms of frequency bands and individual frequencies, were sensitive to highlight
post-concussion sequelae.
Methods
Participants. e inclusion criteria for the participants were adolescents high school athletes aged 14–18
years who were actively participating in football games. Adolescent athletes were emphasized in this study since
according to CDC report, youths are at increased risk of concussion, and 65% of these concussions occur in chil-
dren between 5 to 18 years of age20. ese persons are at a larger risk for traumatic brain injury as their brains are
still young and developing, and the brain tissues are not as able to recover as rapidly as an adult brain21. e data
collection was limited to football to align with the highly broadcasted wave of concerns about the sport-related
brain trauma in National Football League (NFL) stars. Exclusion criteria for the participants included any history
of intellectual or learning disabilities, neurological or psychotic disorders, or alcohol/substance abuse.
Following the inclusion and exclusion criteria, we were able to collect data from a total of 21 male participants
who are football athletes from two high schools available in Grand Forks area. e study was performed following
the experimental protocol approved by the Institutional Review Board (IRB) of the University of North Dakota.
e data were collected in accordance with the guidelines and regulation established by the protocol. e par-
ticipation was voluntary, and the participants had the right to withdraw any time from the study. Informed and
written consent for participation was collected from the athletes and also from their parents or legal guardians.
Each participant had to complete a demographic information form with previous concussion history before data
collection.
Individuals recruited for this concussion analysis study were assigned to a particular group based on the
history of concussion. e healthy group consists of 14 subjects (Age 15.86 ± 0.67 years, Height: 1.75 ± 0.09 m,
Weight: 72.82 ± 10.03 Kg) with no history of concussion while the concussed group has 7 subjects (Age
15.97 ± 0.74 years, Height: 1.77 ± 0.09 m, Weight: 73.20 ± 12.56 Kg) who suered from one or multiple previous
concussions. e concussion was detected by the concussion management team (including athletic trainer and
team physicians) assigned by the respective schools who was present on the sideline during the athletic contest.
e concussion management team detected the concussion by following the established criteria suggested by
American Academy of Neurology Guideline for Management of Sports Concussion22 and state law of North
Dakota23.
All participants were actively participating in sport and athletes with concussion history made a complete
return to play within four weeks of injury. All athletes with a history of concussion (12 days to 15 months from
injury) reported being symptom-free at the time of testing. Control participants were teammates who had never
suered a sport or non-sport related brain injury. Concussed participants’ post-concussion status is shown in
Table1.
From each subject, the traditional assessment data and EEG signals were collected in three dierent trials
with 30-days’ time dierence between the trials. e total number of data collection trials for the healthy group
was multiplied by fourteen (total 42 trials) and for the concussed subject was three multiplied by seven (total 21
trials).
Postural Data Collection Protocol. Balance Error Scoring System (BESS). e BESS is one of the most
popular tests used to nd balance decit in concussed and fatigued athletes24. Postural stability is measured using
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three stances, named double leg stance, single leg stance, and tandem stance. Each test is done on two dierent
surfaces, rst on a rm surface and then on a foam surface. During these stances, athletes’ eyes are closed, and
their hands are placed on the iliac crests and feet positions are dierent based on three distinct stances. Each of
these six subtests is performed for 20 seconds. Deviation from proper stance is referred to an error, and the total
number of errors during the subtests are counted.
Visual Data Collection Protocol. King-Devick (K-D) Test. e K-D test is a test of the visual system and is
based on measurement of the speed of rapid number naming25. e K-D test is faster than other standardized tests
like ImPACT, Military Acute Concussion Evaluation (MACE) and the sports concussion assessment tool (SCAT
3) as it takes just two minutes to complete the testing and thus is more practical in case of sideline application25.
e K-D test consists of three test cards and the athlete’s need to name the numbers from the cards rapidly with-
out any error. e score for the test is calculated by combining the amount of the three times, in seconds, required
to read the three cards. e test involves attention, rapid eye movements as well as language operation. ese
three functions may be adversely aected, resulting in a poor K-D test performance. e test purports to measure
any suboptimal brain functional decits aer a concussion incident, as well as sometimes reects decits due to
sleep deprivation, Parkinson’s disease, hypoxia and multiple sclerosis. In this experiment, we performed the K-D
test to nd out the ecacy of this test to assess suboptimal brain functional decit due to a concussion aer a time
gap between concussion incident and data collection.
Neuropsychological Data Collection Protocol. Immediate Post-Concussion Assessment and Cognitive
Testing (ImPACT). e ImPACT battery is the most common computerized test that can be used in cognitive
concussion assessment26. e test battery consists of three dierent measures: Demographic data, neuropsycho-
logical tests, and the Post-Concussion Symptom Scale (PCSS). e assessment results from these three sections
are combined to assist in accurate evaluation and management of concussion27. e demographic data section
mainly consists of all the important sport, medical, and concussion history related information. For the neuropsy-
chological test sections, ImPACT (version 3.0) contains six dierent neuropsychological tests, and each of these
tests is intended to target dierent parts of cognitive functioning comprising attention, verbal and visual memory,
control, reaction time and processing speed. Combining the results from these six dierent tests, a set of com-
posite scores are produced containing separate measures named verbal memory, visual memory, motor speed,
reaction time and impulse control. e detailed description of these tests can be found at26–28. e last section
named PCSS is also utilized in the ImPACT battery study28. e scale is reported by various sports organizations
to manage and track post-concussion symptoms26–29. is section has a 21-symptom checklist which mainly asks
the athlete to specify a rate for each symptom on a scale of one to seven, with zero representing no presence of a
symptom and six representing a severe symptom. An ImPACT test was performed by all participants during all
three trials.
EEG Data Collection Protocol. EEG activities were measured using a 9-lead wireless B-Alert headset30.
Electrode impedance was kept below 50 k. During data collection, the le mastoid was used as a reference, and
the right mastoid was used as a ground. e sampling rate for data collection was 256 Hz, and data were acquired
by placing nine electrodes at F3, F4, Fz, C3, C4, Cz, P3, P4 and POZ locations as shown in Fig.1.
e data were collected for 5 minutes from all 21 subjects during dierent trial sessions each under three con-
ditions: vigilant task (VT), eyes open (EO), and eyes closed (EC). During VT condition, the subject was highly
engaged by choosing between a primary vs. secondary or tertiary task every 1.5 to 3 seconds. During EO con-
dition, the subject goes through a low engagement state by responding to an optical probe every 2 seconds. e
EC state creates a distraction status, and the subject has to respond to an audio tone every 2 seconds. e same
procedure was followed at all dierent trials for all subjects.
EEG Data Analysis. e EEG data were rst high pass ltered above 1 Hz and then low-pass ltered below
40 Hz, and thus a 1–40 Hz (24 dB/octave) band-pass lter was formed. e rst and last 10 s of each 5-min record-
ing during EO, EC, and VT conditions were rejected to eliminate state transitions. e EEG data were then vis-
ually inspected to determine clean EEG data and randomly occurring large amplitude with power ≥ 3 standard
deviations with respect to the mean value of the clean EEG was removed. Aerward, the stereotypical noise like
eye movements, eye blinks, muscular activity, line noise, motion related signal, and heart signals was cleaned by
Concussed
Participants Number of
concussion Loss of
consciousness Confusion Amnesia
Post-
concussion
RTP days
Days from concussion incident to data
collection
From
incident 1 From
incident 2 From
incident 3
1 2 No Yes Yes 14 263 216 —
2 1 No Yes Yes 21 118 — —
3 1 No No No 7 267 — —
4 3 No Yes Yes 10 462 297 162
5 2 No Yes Yes 25 92 65 —
6 1 No No No 10 127 — —
7 1 No No Ye s 15 12 — —
Table 1. Concussed participants demographic information.
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using well-established Independent Component Analysis procedure of EEGLAB detailed previously31,32. Any
other nonstereotyped or residual artifact was removed through visual inspection of the raw data.
e clean EEG data was then segmented into 1-second epochs containing 256 data points. Power spectral den-
sity (PSD) was determined by computing Fast Fourier Transformations (FFT) with a 10% Hanning window on
each segment to determine spectral power (μV2) for 1 to 40 Hz frequency bins of each EEG channels. e PSD of
the individual bins were then averaged and logged to calculate PSD of conventional EEG frequency bands named
delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz) and gamma (30–40 Hz). Although gamma band
is considered as a pattern of neural oscillation with a frequency between 30 Hz to 100 Hz nowadays, we have used
30–40 Hz as gamma band in this study since the gamma band was previously dened up to 40 Hz33. Numerous
studies reported that the gamma band is most apparent at the frequency of 40 Hz34–38. While most gamma oscilla-
tion study emphasized frequencies around 40 Hz, electrocorticographic recordings (ECoG) in patients enduring
epilepsy have suggested that the functional activation may be more consistently connected with the higher fre-
quencies, typically greater than 60 Hz and may extend up to 200 Hz and beyond39. In our study, the 30 Hz to 40 Hz
was chosen as gamma band since all the analysis of this study is based on EEG and also the interpretation of the
earlier EEG based concussion studies suggested that the EEG spectrum contains some characteristic waveforms
associated with concussion which primarily fall within the frequency band of 1 to 40 Hz15,40–47. Moreover, the use
of high-frequency gamma band is still controversial as studies showed that the change in the higher frequency
gamma-component might be a result of the higher amount of artifact from the electromyographic activity48–50.
Aer calculating the PSD for each channel and bands, overall PSD was calculated by calculating the mean
PSD across all nine referential channels for both individual frequency bins and ve frequency bands. Linear
and nonlinear features were then extracted from the ve frequency bands and also from each of 1 to 40 Hz EEG
frequency bins.
is innovative analysis achieved a new range of frequencies with signicant dierences between healthy
and concussed groups even when the band base analysis was not adequate to reveal the decits. Moreover, in
this paper, we present an exploration of the usefulness of several features for use in concussion detection, which
aims at providing accurate feedback as early as possible. Along with the traditionally used band power estimates,
we computed some time domain as well as nonlinear features from each EEG frequency band and then again
computed all the features from each individual frequency bins. e parameters extracted from EEG signal are
explained as follows.
Linear Features. Power spectral density analysis was performed to extract the linear features from the signal. e
extracted features were; (i) average spectral power for ve frequency bands and (ii) the spectral power for each of
the individual frequency from 1 Hz to 40 Hz.
Time domain Feature. Most popular features used for concussion analysis are EEG band based power spectral
density. In this paper, we introduce new features for concussion assessment called Time Domain Parameters that
are also known as Hjorth parameters. e features are inspired by the fact that they have been previously used
in EEG based experiments like Vidaurre et al. used Hjorth parameters, in their brain-computer interface (BCI)
Figure 1. Experimental setup for EEG data collection. (a) Data collection set up for a participant, (b) Map of 9
Electrodes locations. e locations were plotted using EEGLAB31.
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study51 whereas Cecchin et al. used Hjorth parameters for seizure assessment from raw scalp EEG signals52. e
parameters introduced by Hjorth53 are three features dened as follows by equation1 to 3:
=Activity xt varxt(()) (());(1)
=
()
Mobility xt var
varxt
(()) (()) ;
(2)
dx t
dt
()
=
()
Complexity xt Mobility
Mobility xt
(()) (())
(3)
dx t
dt
()
The first parameter, Activity, calculates the alteration of time signal and characterizes the signal power.
Mobility is computed by calculating the square root of the variance of the rst derivative of the signal divided by
the activity and thus species the average frequency or proportion of standard deviation of the spectral power.
Complexity describes the change in frequency by comparing the Mobility of the rst derivative of the signal
with the signal’s mobility, and for more resemblance between the signals, the value converges to one. ese three
parameters consider the frequency component of the signal itself and thus remain more robust against the errors
due to overtting or non-stationarities of the signal52. To reduce the complexity of calculation, these three param-
eters were calculated in a stationary mode of signal separately for each EEG channel of the entire signal. us, the
extracted parameters were three features per channel and, as a whole, a feature vector for each parameter. A total
of 27 features (3 features for nine channels) were extracted and then averaged for all channels.
Nonlinear Features. Dierent nonlinear parameters have been shown signicantly useful in the diagnosis of
neurological disorders. Nonlinear parameters like approximate entropy (ApEn), Hurst exponent, and Correlation
dimension have been used for automatic diagnosis of seizure onset and reported as a promising approach in dif-
ferentiating normal, pre-ictal and epileptic seizure from EEG signals54.
In the eld of cortical neuronal dynamic study, the existence of long-range temporal correlation (LRTC) is
considered a potential observed phenomenon as it is proven to be gradually reduced with the power-spectrum55.
e LRTC property of an amplitude-time signal has vital importance as it is found to have a relationship with
the distributed neural network33. Poil et al. reported the coexistence of LRTC property of amplitude time series
with neuronal avalanche activity56, and thus recommended a relationship between oscillatory activity detected
in the EEG and the criticality hypothesis56,57. Using these hypotheses, Shew et al. suggested a possible connection
between optimal functioning and LRTC in the amplitude of oscillations35. Moreover, the signicance of the LRTC
property has also been proven in numerous clinical studies linking a number of neuronal diseases (including
schizophrenia58, Alzheimer’s disease59, major depressive disorder60, and epilepsy61) with altered LRTC properties.
To quantify the degree of change in LRTC property in a signal, the Hurst exponent (H), (explained in a later para-
graph) is measured55. Hurst exponent was used by Holler et al. for the disorder of consciousness studies62 whereas
Culic et al. reported this property to be important to dierentiate epileptic patients63.
Another nonlinear parameter that was calculated was ApEn. ApEn is a widely known mathematical algorithm
which computes the predictability of time series data by quantifying the regularity and complexity of the signal.
ApEn quanties the logarithmic likelihood of the patterns in the signal that remain close on next incremental
comparisons64.
Values of the ApEn parameter have been reported signicantly dierent between EEGs collected from epilep-
tic seizure patients and normal EEG signals65. Guo et al. present a method based on approximate entropy for clas-
sifying the EEG regarding the existence and absence of seizures using the neural network with 99.85% accuracy66.
Inspired by these publications, we tested the ecacy of these features to distinguish healthy and concussed
athletes in this study. Approximate entropy (ApEn) and Hurst exponent were extracted as the nonlinear features
to measure synchrony and complexity of the EEG signal as explained in the following sections.
Approximate Entropy. ApEn was calculated for each frequency (1 to 40 Hz) and for each of ve frequency bands
of EEG data for all three dierent conditions in order to nd out if there was any relationship between the ran-
domness of EEG data along with a concussion. A lower value of approximate entropy species that the EEG data
is more deterministic whereas a higher value of ApEN determines the data is more random. is feature was
calculated using the ApEn function provided by Kijoon Lee in the MATLAB central le exchange67. e tolerance
chosen for ApEn calculation was two standard deviations.
Hurst Exponent. e Hurst exponent (H) calculates the extent information presented by a signal is related to
the history of the signal. e value of H varies from 0 to 1; 0 < H < 0.5 indicates the samples in the signals are
far apart and independent and thus the signal is short-range dependent. However, if 0.5 < H < 1, then the value
is said to contain LRTC, with higher values of H representing a stronger LRTC property55. e Hurst exponent
is thus known as the index of long-range dependence68. e value of H was calculated for each channel over the
entire EEG signal. A total of 9 components for 9 EEG channels were extracted for each signal.
Statistical Analysis. e decits between healthy and concussed groups were veried using statistical anal-
ysis, and the measurements were performed without knowledge of groups. e Shapiro-Wilk test was applied
to ascertain the normality of the data. For normally distributed data, a two-tailed Student t-test, followed by
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Bonferroni’s post hoc test when applicable was implemented; otherwise, Wilcoxon rank sum test was considered.
e values in the manuscript are presented as mean ± standard deviation format with statistical signicance level
set at (p = 0.05). e test of signicance was performed using the MATLAB Statistical Toolbox69.
Data Availability. e database generated and/or analyzed during the current study will be available from
the corresponding author upon request.
Results
BESS Test. Postural decits in terms of the BESS associated with concussion showed no signicant dierence
between healthy and concussed group. Average sway per second was calculated using a modied Wii balance
board during the BESS assessment for healthy group (group average sway = 3.28 ± 0.69 cm) and concussed group
(group average sway = 3.00 ± 0.72 cm). e number of average BESS errors reported by the healthy group were
thirty compared to thirty-four reported by the concussed group. ough the average sway scores exhibited by
both groups were quite similar, the concussed group reported more errors than their healthy matched controls.
e t-test resulted in no signicant dierences (Average sway: p-value = 0.33, Number of errors: p-value = 0.39)
between the groups regarding average sway and number of errors.
K-D Test. K-D test measures the deciencies of attention and eye movements by capturing the speed of rapid
number naming. e athletes who sustained concussions required slightly more time to complete the task than
their peers in the healthy group (by approximately 0.1%), but the decits did not reach a level of signicance
(Healthy group: 53.20 ± 10.33, Concussed group: 53.74 ± 10.29; p-value = 0.966).
ImPACT Test. e healthy and concussed groups were not signicantly dierent with regard to age but were
signicantly dierent based on the number of prior concussions. A two-tailed t-test was performed to evaluate
the dierences in neuropsychological test performance regarding ImPACT battery between the concussed and
control groups. Table2 presents the detailed descriptive statistics for verbal and visual memory, processing speed,
and reaction time composite scores.
ough a number of studies reported the ability of the ImPACT to dierentiate healthy and concussed groups,
our analysis revealed no signicant dierence in any composite scores between the groups.
Neuronal Deficits in Terms of EEG Band-Power following Concussion. The EEG analysis was
conducted to extract the neuronal decits following a concussion. Athletes in the concussed group exhibited
an increase in delta and theta bands, and a decrease in alpha, beta and gamma frequencies compared to their
uninjured peers during all three testing conditions (Table3). As indicated in Table3, the dierence reached the
signicance level for the increase in delta band and decreased in alpha, beta and gamma frequency bands for all
three conditions.
Neuronal Decits in Terms of EEG Individual Frequency Power following Concussion. is anal-
ysis considered individual EEG frequencies to nd gaps between healthy and concussed groups. Figure2 shows
the results of both frequency band and individual frequency analysis for three experimental conditions (EO, EC,
and VT). e dashed black line shows the condence level of p = 0.05. e solid red lines show the p-value for
each frequency band (delta, theta, alpha, beta, and gamma bands). e bars in each frequency band show the
p-value for individual frequencies. To highlight the subject to subject variance for each group, a supplementary
table with the mean and standard deviation of power of each signicant frequency bins for each group is added
to the manuscript.
e athletes who sustained a concussion had a range of frequencies with a signicant dierence from the
healthy group during EO condition (1–3 Hz, 9–10 Hz, 20–24 Hz, 27–30 Hz, and 33–38 Hz) as shown in Fig.2(a).
A very similar, but not all range of signicance was exhibited during EC condition (1–3 Hz, 9–10 Hz, 15–18 Hz,
20–24 Hz, 28–30 Hz, and 35–38 Hz) as shown in Fig.2(b). e signicant individual frequencies exhibiting the
decits between healthy and concussed groups during VT condition were (1–3 Hz, 6–7 Hz, 9–10 Hz, 19–30 Hz,
34–38 Hz) and were much consistent with EO condition as shown in Fig.2(c).
Neuronal Decits in terms of Nonlinear Features from EEG Individual Frequency following
Concussion. In the nal analysis, nonlinear features were calculated in order to nd out if new features
Composite Scores
Healthy Group Concussed Group
F value p-Va lueMean ± SD Mean ± SD
Verbal Memory Index 89.93 ± 7.87 87.57 ± 9.25 0.58 0.58
Visual Memory Index 86.43 ± 5.37 82.57 ± 7.79 0.25 0.27
Motor Speed Index 40.36 ± 5.81 37.28 ± 5.37 0.75 0.20
Reaction Time Index 0.62 ± 0.09 0.65 ± 0.12 0.38 0.59
Impulse Control Index 5.86 ± 3.21 6.14 ± 2.9 0.86 0.84
Total Symptom Score Index 2.93 ± 2.13 3.14 ± 3.53 0.12 0.88
Table 2. Group means and standard deviations for ImPACT composite scores of healthy and concussed groups.
e test of signicance was performed with statistical signicance level of 0.05.
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extracted from the EEG data can tabulate the deciencies due to a concussion. e extracted features were
approximate entropy, activity, mobility, complexity and Hurst exponent features. While calculating these fea-
tures for EEG frequency bands (delta, theta, alpha, beta, and gamma), no signicant decits were found between
healthy and concussed athletes. But when the analysis was done for individual frequencies instead of frequency
bands, interesting outcomes were exhibited. A set of individual frequencies was found for each nonlinear feature
which can reveal signicant decits between healthy and concussed athletes as reported in Fig.3. As shown in
Fig.3(a) for EO condition, the frequencies indicating signicant decits between healthy and concussed groups
in terms of 2 or more nonlinear features are 1–2 Hz, 8 Hz, 19 Hz, 21 Hz, 23 Hz, 25–26 Hz, 31 Hz, 34 Hz and 37 Hz.
For EC condition, Fig.3(b), the range of frequencies with decits in two or more features was for 1–2 Hz, 13 Hz,
16 Hz, 34 Hz and 37 Hz, and for VT condition, from Fig.3(c), the range was 1–2 Hz, 8 Hz, 15–16 Hz, 23 Hz, 26 Hz,
31 Hz, 34–35 Hz and 37 Hz. e most ecient nonlinear features to reveal deciency following concussion were
approximate entropy, activity and Hurst exponent feature.
Discussion
Residual damage to the brain due to concussion can oen evade clinical detection. Enhancing ways in which con-
cussion is assessed is pivotal, specically in susceptible individuals such as adolescent athletes where functional
decits can be elusive and seriously underreported. Better assessment is also essential since early identication of
the signs of a concussion can progress positive outcomes and thus suggests that there is a clear need for an eec-
tive evaluation approach to eciently assess and quantify high-risk individuals such as athletes who may have
already sustained a concussion. e current study aims to test the hypothesis that the concussion disrupts the
normal brain activities of a person. To detect these decits, we combined the BESS, K-D test, ImPACT, and EEG
analysis to capture the postural, suboptimal, neurophysiological and neuronal decits following a concussion.
Evidence from the previous studies29,40,70 shows that the cognitive impairment regarding the BESS is most
pronounced during the time of injury and 24 hours post injury but appears to resolve by day ve aer a concus-
sion incident. e balance decit through the BESS in our research resulted in no signicant dierence between
the healthy and concussed group and thus strengthened the already established hypothesis26,40,70 that the postural
decits resolve within a brief period post-injury and therefore may suggest that the BESS is not sensitive enough
to interpret any residual decits associated with long-term concussion history.
As expected, the K-D test, which is mainly a rapid screen tool and typically used immediately aer concussion25,
was unable to detect any decits in our study. is can be explained by the fact that the related visual decits due
to a concussion were resolved during the several months’ time gap between the concussion incident and data
collection.
e ImPACT was reported by multiple sports-related concussion studies as a potential tool to detect the
impaired neurocognitive functioning due to concussion26–28. Also, some studies showed neuropsychological base-
line assessment models like ImPACT could assist the diagnosis of subtle neurocognitive deviations in athletes
aer a concussion incident26,27. ough several studies demonstrated that a history of concussion is associated
with poorer performance in ImPACT71, the role of concussion history remains a controversial issue, with various
studies yielding no relationship between concussion history and ImPACT performance28. e results of this man-
uscript suggest that there is no signicant eect of a history of concussion associated with performance measured
by ImPACT, which is understandable, as ImPACT is an immediate post-concussion paradigm, and due to the
long time gap between concussion incident and data collection, the sensitivity of the test deteriorates with time.
To capture the signature neuronal decits exhibited by concussed athletes that distinguish them from their
healthy peers, we evaluated several approaches utilizing a set of linear, time-frequency based features along with
nonlinear features extracted from EEG signals. In conjunction with band base analysis, this study undertook a
systematic exploration to nd out the decits within specic frequency bins from 1 to 40 Hz. e system works by
following four main steps: data acquisition, data preprocessing, feature extraction (power spectral, time domain
and nonlinear) and statistical analysis (functional decits detection).
For band base analysis, EEG was divided into traditional frequency bands (delta, theta, alpha, beta, and
gamma). Aer normalization, power spectral density analysis revealed a signicant dierence between healthy
and concussed athletes. ere are several ndings of interest. First, the PSD features collected from frequency
sub-bands played an important role in distinguishing concussed individuals. Discriminative features were
observed in delta, alpha, beta and gamma frequency bands. A dierence was also noted in theta frequency band.
Condition Participants
Delta (μV2)eta (μV2)Alpha (μV2)Beta (μV2)Gamma (μV2)
Mean ± SD Mean ± SD Mean ± SD Mean ± SD Mean ± SD
EO Condition Healthy 4.33 ± 0.25 3.47 ± 0.33 3.14 ± 0.36 2.48 ± 0.30 1.95 ± 0.15
Concussed 4.81 ± 0.34*3.67 ± 0.38 2.69 ± 0.25*2.14 ± 0.18*1.58 ± 0.21*
EC Condition Healthy 4.21 ± 0.33 3.43 ± 0.28 3.22 ± 0.23 2.46 ± 0.26 1.92 ± 0.13
Concussed 4.66 ± 0.30*3.59 ± 0.33 2.85 ± 0.34*2.13 ± 0.33*1.50 ± 0.34*
VT Conditi on Healthy 4.22 ± 0.24 3.38 ± 0.49 3.09 ± 0.40 2.47 ± 0.27 1.97 ± 0.17
Concussed 4.68 ± 0.45*3.58 ± 0.34 2.65 ± 0.28*2.08 ± 0.29*1.52 ± 0.21*
Table 3. EEG band power decits between healthy and concussed group for eyes open (EO); eyes closed (EC)
and vigilant task (VT) conditions. (*Denotes signicant dierences between healthy and concussed group at
statistical signicance level of 0.05).
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It should be pointed out that similar frequency bands were targeted in some previous EEG studies of concus-
sion47,72,73. An increase in delta and theta frequency and a decrease in beta frequency were also reported by
McCrea et al.40 and Slobounov et al.72. e discrimination at reported by dierent frequency bands can indicate
signicant neuronal dysfunction. According to Demos et al.73, an increase in delta frequency may indicate brain
injuries, learning problems, or diculties with cognition. e decrease in alpha band power exhibited through
the analysis partially overlaps with the results reported by atcher et al. in a previously conducted mTBI based
study where coherence, phase, and power analysis was performed on EEG data collected from 130 participants43.
e decrease in alpha power exhibited by concussed athletes compared to control peers may be interpreted as a
reection of reduced cortical excitability74. A substantial decrease in beta and gamma power was also revealed
by the analysis. Certain levels of beta waves allow easy focus and involvement in conscious thought and logical
Figure 2. P-value vs. frequency plot. A set of individual frequencies from EEG data exhibits power spectral
density decits between healthy and concussed athletes. e X-axis in the gure shows the individual
frequencies and Y-axis shows the level of signicance. e color of bars is dierent based on each frequency
band, and the level of signicance for each EEG frequency band is shown by red lines. e p-value vs. frequency
is shown during three conditions (a) eyes open (EO) (b) eyes closed (EC), and (c) vigilant task (VT). All the test
of signicance was performed with statistical signicance level of 0.05.
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thinking, whereas a decrease in beta waves may point to poor cognition, diculty in concentration73. Moreover, a
movement plan based study in terms of reaction time and endpoint error reported that a decrease in beta power is
correlated with higher end point error75. A study conducted by Kwon et al. demonstrated a reduced gamma power
by schizophrenia patients and concluded that the decit might reveal a less eective local neuronal synchroni-
zation to external stimuli in the thalamic sensory oscillations or in the sensory cortex76. A decrease in gamma
power was also reported to be correlated with lower consciousness in the anesthesia study conducted by Pritchett
et al.77. Several studies also reported that a decrease in gamma power is frequently related to an increase in the
low-frequency range (delta frequency band) power78,79 and interpreted to be related to lower neuronal activity of
the brain region that operates to generate behavior80. All these specic power increases in the slower frequency
band (delta), combined with the decrease of power in faster frequency bands (alpha, beta, gamma) exhibited by
concussed athletes may imply that their neurological status is not as sound as their healthy matched peers in the
control group.
ough a lot of studies revealed signicant dierences in EEG sub-bands, there is no signature prole to
indicate increase or decrease of band powers associated with concussion. at’s why the pathophysiology of con-
cussion is considered heterogeneous and not yet completely understood. To reinforce our EEG-based functional
decits hypothesis, in an innovative approach, the PSD based analysis for each of the EEG individual frequencies
was conducted. Aer analyzing 189 cases, i.e., three dierent trials in three dierent conditions (EO, EC, VT) for
21 subjects as shown in Fig.2, it was concluded that four ranges of frequencies are more ecient in highlighting
decits following a concussion. ese ranges are slow delta (1–2 Hz), slow alpha (9–10 Hz), fast beta (20–30 Hz)
and fast gamma (34–39 Hz). A similar individual frequency-based analysis conducted by us on eyes closed EEG
collected from a dierent dataset of 20 healthy and 20 immediate concussed athletes also resulted in a nearly simi-
lar range of frequencies (1–2 Hz of delta band, 8–10 Hz of the alpha band, 24–29 Hz of the beta band and 34–36 Hz
range within the gamma band)41. To date, no individual frequency based study was conducted for concussion
Figure 3. Frequencies with a signicant dierence in approximate entropy, activity, mobility, complexity and
Hurst exponent between healthy and concussed athletes for three conditions: (a) eyes open (EO), (b) eyes
closed (EC), and (c) vigilant task (VT). All the test of signicance was performed with statistical signicance
level of 0.05.
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assessment, and more collaborative research is needed to establish a direct relationship of these frequency bins
with a concussion. e decrease in alpha band frequency bins exhibited through individual frequency analysis
partially overlaps with the results reported by atcher et al. in a previously conducted mTBI based study43. An
increase in theta band frequency bins during VT task may be associated with ADHD, depression, hyperactivity,
impulsivity, and inattentiveness51. e individual frequency-based analysis also revealed signicant dierences in
the upper level of beta bands compared to the lower level frequency bins. Oscillatory activity in the beta band was
previously reported to reect the presence of inhibition of the process of ongoing motor task81.
Elgendi et al. demonstrated an Alzheimer disease (AD) study and reported that new optimized frequency
ranges (4–7 Hz, 8–15 Hz, 19–24 Hz) resulted in better classication accuracy than the traditional frequency bands
for the diagnosis of AD82. Similarly, if we consider the neurological decits observed in individual frequency bins,
as well as in the conventional frequency bands as a whole, the most reliable interpretation is that these decits
may be a consequence of their injury and can possibly be used as a concussion assessment index to identify the
concussed athletes at the time of injury or during the post-concussion recovery period.
In the second phase of this study, a set of time-domain and nonlinear features were extracted. ese features
have been proven to be suitable to characterize neurological disorders like epilepsy, attention-decit/hyperactivity
disorder (ADHD) and Alzheimer disease in the literature83. It was hypothesized that the time domain and nonlin-
ear feature based study could reveal new aspects and provide more information regarding the complex and cha-
otic nature of the EEG data. As reported by Mohammadi et al.84, quantitative measures of chaos and non-linear
features are convenient descriptive tools to characterize electrophysiological abnormalities in neuropsychiatric
disorders that are not evident in linear analysis. To show the eectiveness of these features for a concussion, in
a similar approach to power analysis, the features were calculated for both frequency bands and individual EEG
frequencies. ough the concussed athletes exhibit dierent values for Hjorth time domain parameters and non-
linear parameters like approximate entropy and Hurst exponent, none of the parameters showed a signicant
dierence compared to their healthy peers for traditional EEG frequency bands. But, when the analysis was done
for each frequency, it was noted that signicant dierences were observed for certain frequencies as shown in
Fig.3(a–c).
e observation of signicantly dierent nonlinear features also revealed important notions about concussed
athletes. e concussed athletes exhibited a decrease in Hjorth complexity and mobility. It has been reported by
Pezard et al.85 that depressive subjects tend to display lower complexity than controls. Moreover, Hamida et al.86,86
reported the decreased complexity and mobility are associated with insomniac subjects. Approximate entropy
quanties the amount of regularity in data by calculating the upcoming amplitude values of the signal based
on the knowledge of the preceding amplitude values87. Sohn et al.88 reported a signicantly lower approximate
entropy for a group of ADHD subjects compared to matched controls and hypothesized that the patients might
not have sucient levels of cortical activation to reach the requirements of attention-demanding tasks. Following
their hypothesis, a signicant decrease in approximate entropy exhibited by concussed athletes may point out that
their cortical information processing is altered compared to healthy athletes. Moreover, many pathological dis-
order studies like schizophrenia, posttraumatic stress disorder, panic disorder, and epilepsy reported lower com-
plexity in pathological states compared to healthy subjects89. e notion claimed by the authors is that the lower
EEG complexity is attributed to the abnormal neural integration in the above-mentioned mental disorders58 and
thus a lower value of ApEn demonstrated by concussed athletes in our study implies that they may still have some
irregularity in their neural integration.
Another nonlinear feature with a signicant dierence was the Hurst exponent. Higher values of Hurst expo-
nent indicate a stronger long-range temporal correlation of amplitude uctuations of EEG55. In accordance with
the result reported by Geng et al.90 in their epileptic study, a decreased Hurst exponent exhibited by concussed
athletes in our study implies that the degree of anti-correlation of concussed athletes is larger than that of healthy
athletes.
The most efficient frequencies indicating the deficits were found to be 1–3 Hz, 21–24 Hz, 28–30 Hz and
35–38 Hz. Among the EEG task condition, EO and VT conditions were found to be more ecient in identifying
hidden decits due to a concussion. ough conventional band base analysis revealed no signicant dierence
between healthy and concussed athletes regarding time domain and nonlinear features, individual frequency
analysis was ecacious to exhibit these hidden discrepancies. ese dierences at specic frequencies would
remain unnoticed if only conventional frequency bands were considered. Ultimately, this study exposed the fact
that EEG analysis for each frequency is equally as important as conventional bands to evaluate the neurological
dysfunction following a concussion.
Conclusions
is study suggests that EEG analysis is more sensitive compared to cognitive testing to decipher persistent seque-
lae of sport-related concussion. For the rst time, a set of time domain and nonlinear EEG features was utilized in
addition to the standard frequency band features to highlight neuronal decits following a concussion. Also, the
approach of analysis using individual frequencies of EEG was conducted for the rst time to study concussion.
is innovative approach combined with novel features opens a new door to interpret subtle post-concussion
decits. While no previous work was done to nd the post-concussive decits in individual frequency level, the
result demonstrated a new range of frequency which is more successful to reveal the discrepancies. In sum, accu-
mulated evidence from this study suggests that the proposed approach of EEG analysis was successful to identify
that the athletes with a history of concussive injury still exhibited neurological alterations, despite reporting to be
symptom-free by standard postural, visual or neurophysiological tests.
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Limitations and Future Work. Although the current manuscript has several strengths, some limitations
should be considered. First, the analysis was cross-sectional, and it is always possible that some unmeasured vari-
able may add to the current group alterations. is probability is minimalized, however, as the study groups were
co-players cautiously matched for weight, height, age, years of education, and sport. Although we repeated the
experiment in three separate sessions and using three dierent conditions, the data set is small and was limited
to male athletes only. As such, the conclusions drawn from the current dataset should be used to guide similar
studies on larger datasets and other age groups. However, this is an ongoing project, and we are collecting data
from more participants so that more rigorous quantitative and qualitative analysis can be performed with a larger
data set consisting of recordings from a large number of subjects in the future. Future work would also include
applying the proposed methodology for the classication of two classes, namely healthy and concussed, to detect
and predict the concussion from EEG signals for the normal and abnormal condition. erefore, our ndings
will engender more comprehensive evaluations towards clinical applicability of concussion assessment for proper
diagnosis and prevention through accurate RTP decision, as well as managing the treatment and rehabilitation
ecacy post-concussion.
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Acknowledgements
Financial support from University of North Dakota Faculty and Collaborative Seed Money is gratefully
acknowledged. We also gratefully acknowledge Jerey Gendreau and Jessica I. Herren for their assistance in data
collection. In addition, we would like to thank Dr. Colin Combs and Dr. Kumi Nagamoto-Combs from School
of Medicine and Health Sciences of University of North Dakota for their suggestions in the initial study design.
Author Contributions
T.T.K.M., A.H., M.R. and R.F-R. conceptualized the research. M.R. and R.F-R. designed the experiment. C.S. and
M.R. performed the experiment; T.T.K.M., and A.H. conducted the formal analysis; T.T.K.M.,A.H., and R.F-R.
discussed and interpreted the results;T.T.K.M., A.H., and R.F-R. wrote the original dra; All authors reviewed
and edited the nal manuscript.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-17414-x.
Competing Interests: e authors declare that they have no competing interests.
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