Available via license: CC BY-NC 3.0
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Received: 30 May, 2015 Revised: 14 June, 2015 Accepted: 16 June, 2015
Corresponding a uthor: Lee Berk
Clinical Molecular Research Lab. A117, Department of Allied Health Professions, School of Allied Health Professions, Loma Linda University, 11234 Anderson
St. Loma Linda, CA 92350, USA
Tel : 1-909-651-5828 Fax: 1-909-558-0481 E-ma il: lberk@llu.edu
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licens
es/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © 2015 Korean Academy of Physical Therapy Rehabilitation Science
http://dx.doi.org/10.14474/ptrs.2015.4.1.28
Phys Ther Rehabil Sci
pISSN 2287-7576
2015, 4 (1), 28-31
eISSN 2287-7584
www.jptrs.org
Electroencephalographic brain frequency in athletes differs
during visualization of a state of rest versus a state of exercise
performance: a pilot study
Lee Berka,b,c, Deeti Malib, Gurinder Bainsa, Bhagwant Madaneb, Jessica Bradburna, Ruchi Acharyab,
Ranjani Kumarb, Savleen Junejab, Nikita Desaib, Jinhyun Leeb, Everett Lohmanb
aDepartment of Allied Health Professions, School of Allied Health Professions, Loma Linda University, Loma Linda, CA, USA
bDepartment of Physical Therapy, School of Allied Health Professions, Loma Linda University, Loma Linda, CA, USA
cDepartment of Pathology, School of Medicine, Loma Linda University, Loma Linda, CA, USA
Objective:
Psychomotor imagery has been widely used to improve motor performance and motor learning. Recent research sug-
gests that during visualization, changes occur in neurophysiological networks that make physical practice more effective in con-
figuring functional networks for skillful behaviors. The aim of our pilot study was to determine if there was change and to what ex-
tent there was differentiation in modulation in electroencephalography (EEG) frequencies between visualizing a state of rest and a
state of exercise performance and to identify the preponderant frequency.
Design:
Quasi-experimental design uncontrolled before and after study.
Methods:
EEG brain wave activity was recorded from 0-40 Hz from nine cerebral cortical scalp regions F3, Fz, F4, C3, Cz, C4,
P3, POz, and P4 with a wireless telemetric EEG system. The subjects, while sitting on a chair with eyes closed, were asked to visu-
alize themselves in a state of routine rest/relaxation and after a period of time in a state of their routine exercise performance.
Results:
The gamma frequency, 31-40 Hz, (γ) was the predominant wave band in differentiation between visualizing a state of
rest versus visualizing a state of exercise performance.
Conclusions:
We suggest these preliminarily findings show the EEG electrocortical activity for athletes is differentially modu-
lated during visualization of exercise performance in comparison to rest with a predominant γ wave band frequency observed dur-
ing the state of exercise. Further controlled experimental studies will be performed to elaborate these observations and delineate
the significance to optimization of psychomotor exercise performance.
Key Words:
Athletes, Electroencephalography, Exercise, Rest, Visualization
Introduction
Recent research shows that neurophysiological correlates
of visuo-motor learning occur through mental and physical
practice [1]. In addition the research has shown a facilitated
effect of real-time cortical feedback in motor imagery-based
mental practice training [1]. This suggests that during visu-
alization, changes occur in neurophysiological networks that
make physical practice more effective in configuring func-
tional networks for skillful behaviors [2]. Gamma brain-
waves, 31 Hz to 40 Hz, (γ) are considered to be a frequency
related to optimal brain function. Gamma frequencies are
observed in most brain areas and serve as a “binding” mech-
anism or synchronization between regions, helping to im-
prove cognitive processing, memory, sensory perception
and peak performance, both physical and mental [3-5].
Original Article
Berk, et al: EEG brain frequency in athletes-visualization of rest versus exercise
29
Table 1. Baseline characteristics of subjects
Characteristic Mean
Age (y) 27
Height (cm) 179
Weight (kg) 72
Body mass index (kg/m2)22
Average hours of sleep each day 7
Average hours of exercise each day 1
Figure 1. The nine cerebral cortical scalp regions were F3, Fz, F4,
C3, Cz, C4, P3, POz, and P4.
Table 2. Rest visualization: heart rate and
b
lood pressure data
p
re-rest (baseline) and post-rest
Subject 1Subject 2Subject 3
Pre-rest HR (beats/min) 53 49 51
Post-rest HR (beats/min) 57 49 51
Pre-rest BP (mm/Hg) 139/81 122/72 107/72
Post-rest BP (mm/Hg) 152/93 124/70 111/71
HR: heart rate, BP: blood pressure.
Methods
Subjects
Three healthy “high level” athletes (2 females, 1 male)
who exercised daily to every other day and free of any im-
mune, orthopedic, or neurological pathological condition
were recruited. The subjects were recruited from a CrossFitⓇ
exercise program near Loma Linda University, Loma Linda,
CA, USA. All study subjects were 8 hours NPO and were
tested between 8 and 9 am to control for circadian rhythm.
Baseline demographic characteristics of subjects includ-
ing age, height, weight, body mass index, average hours of
sleep, and exercise per day are given in Table 1.
Procedures
Upon arrival, the initial measurements of heart rate (HR)
and blood pressure (BP) were obtained. A repetition of three
HR and BP measurements was recorded and the mean was
reported. The B-AlertⓇ×10 telemetric electroencepha-
lography (EEG), ten-channel system (Advanced Brain
Monitoring Inc., Carlsbad, CA, USA) was used to acquire
the EEG data. Following the 10-20 system, as shown in
Figure 1, nine cerebral cortical channels (F3, Fz, F4, C3, Cz,
C4, P3, POz, and P4) were used in this study. The reference
electrodes were placed on mastoids, under the right collar
bone (clavicle) and on the left fourth intercostal space on the
chest and the electrode impedance was kept below 40 kΩ.
Eye blinks and excessive muscle activity were identified and
decontaminated by the system.
The B-AlertⓇ×10 wireless EEG nine-channel headset
was placed on the scalp as well as appropriate placement of
reference electrodes. Electrode impedance was then
checked.
To determine normalcy of EEG brain wave frequency per
subject, baseline EEG data testing was performed. Individ-
uals’ EEG brain wave patterns were assessed during per-
formance of a 3-choice psychomotor vigilance task (3CVT).
Tests used were the baseline alertness and memory profiler
(AMP). The AMP obtains 5 minutes each of: a 3CVT, eyes
open, and eyes closed. Baseline data was collected once for
each individual before beginning the actual tasks of rest and
exercise visualization. During the tasks of visualizing rest
and exercise, the EEG B-AlertⓇ×10 was recording the brain
wave activity. The power spectral density μV2 (PSD) of
wave band frequencies Delta δ (1-3 Hz), Theta θ (3-7 Hz),
Alpha α (8-13), Beta β (13-30 Hz) and Gamma γ (31-40
Hz) were analyzed for each task. The subjects started with
task of visualizing rest, where they were asked to visualize
that they were their favorite place for resting for 2 minutes.
Post-rest HR and BP were recorded immediately following
rest. The subjects were then asked to visualize that they were
performing their regular exercising routine for a 2 minute
period. Post exercise HR and BP were taken immediately
following the exercise task and again at an interval of 3 mi-
nutes and 5 minutes.
Results
Table 2 shows HR and BP data at pre-rest (baseline) and
post-rest visualization task. Table 3 shows the HR and BP
data post-exercise visualization.
EEG PSD was acquired for 0-40 Hz for all nine cerebral
30
Phys Ther Rehabil Sci 4(1)
Table 3. Exercise visualization: heart rate and
b
lood pressure
data pre and post-exercise
Subject 1 Subject 2 Subject 3
Pre-exercise HR (beats/min)
Post-exercise HR (beats/min)
Immediate post
3 mins post
5 mins post
Pre-exercise BP (mm/Hg)
Post-exercise BP (mm/Hg)
Immediate post
3 mins post
5 mins post
53
56
45
46
139/81
147/89
140/85
137/92
49
54
52
49
122/72
121/68
115/74
122/67
51
55
57
51
107/72
111/74
120/73
105/74
HR: heart rate, BP: blood pressure.
Figure 2. Represents the power spectral density of the frequencies showing the overall pattern for the nine electroencephalography cerebral
cortical sites assessed. GM: grand mean.
cortical regions. PSD γ frequency was the predominant
brainwave frequency observed in this pilot study. We ob-
served that the left side of brain (F3, C3, P3) exercise visual-
ization increased PSD γ frequency 314% compared to rest
visualization, right side of brain (F4, C4, P4) exercise visual-
ization increased γ frequency 465% compared to rest, and
for midline brain (Fz, Cz, Pz) exercise visualization in-
creased γ frequency 465% compared to rest. Also, for the
left side of brain, γ frequency was greater for exercise ver-
sus rest visualization by 70.5% and 25%, respectively as
compared to right side.
These results show that γ frequency for visualization of
exercise is greater for all areas of the scalp region assessed
except for F4 during visualized states of exercise compared
to rest. In addition, Figure 2 shows all frequencies (0 to 40
HZ) for δ, θ, α and β.
The differences in PSD for γ frequency comparing visu-
alization of rest with visualization of exercise are shown in
the heat maps in Figure 3.
Berk, et al: EEG brain frequency in athletes-visualization of rest versus exercise
31
Figure 3. Heat Maps showing in-
creased gamma activity in the
b
rain
during rest visualization task com-
pared to exercise visualization task.
GM: grand mean.
Discussion
This study shows the difference in brain activity with EEG
monitoring during the visualization of the task of rest versus
visualization of the task of exercise to the best of our knowl-
edge, this is first study that has observed the differences in
brain state during visualization of rest and exercise. PSD γ
frequency was the predominant brainwave observed in this
pilot study and hence our research questions focused on γ
frequency modulation.
These findings are preliminary, but suggestive that mental
visualization can be an effective support in the configuration
of brain neural functional networks for performing skillful
behaviors [6]. These visualization of exercise findings sug-
gest possible neuronal reinforcement of neurophysiological
networks. It appears that the neurophysiological and cogni-
tive correlates are associated with predominance of PSD in
“whole-brain” of the γ frequency.
Further controlled experimental studies need to be per-
formed to elaborate these observations and delineate their
significance to optimization of psychomotor exercise per-
formance and therapeutic outcome [7]. Studies need to be
conducted on larger cohorts with different athletic levels of
conditioning. Although our study focused on gamma fre-
quency but further research should be conducted to study
possible variation in other EEG brain wave frequencies.
Therefore, further expanded controlled studies will be con-
ducted to elaborate our pilot observations and attempt to de-
lineate the significance for optimization of psychomotor ex-
ercise performance.
Conflict of Interest
The authors declared no potential conflicts of interest
with respect to the authorship and/or publication of this
article.
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