Virtual Reality Therapy for Mental Stress Reduction

Abstract

Introduction: Mental stress is a perilous condition seen in students that is often overlooked in the daily course of life. With the advent of Virtual Reality Technology (VRT) and older techniques being cumbersome and hazardous, Virtual Reality Therapy is coming to the fore as a possible replacement in the field of rehabilitation and psychiatry. Aim: To understand the role of VRT and its effectiveness in mitigating mental stress in students. Materials and Methods: A sample size of 20 from a study population of 65 healthy, right-handed, and non-placed participants in their final year from the department of Biomedical Engineering were separated equally into normal and mildly stressed groups based on their DASS scores. A custom made Virtual Environment was used for therapy and an experimental protocol was employed. The Electroencephalogram (using RMS SuperSpec) and Mathematical Go-NoGo Task Performance results before and after the therapy were used to quantify the effectiveness of the therapy. The Mann-Whitney U-Test was used for independent sample analysis of the DASS scores between the Normal and Mildly Stressed Groups, and the Post-hoc Wilcoxon Signed-Rank Test was used for related sample analysis of EEG and Task Performance during Rest, Task before therapy and Task after therapy. Results: Though there was no significant difference in the DASS scores between the two groups, there was a sense of relaxation being imbibed into each group after therapy, by virtue of increased mean alpha and decreased mean theta band power in the EEG signals and also an increase in their task performance after therapy. Conclusion: Thus, this study shows the possible ability of VRT in mitigating stress in the participants, and further studies between various levels of stress and using various environments could help establish VRT as a staple in the field of psychiatry for years to come. © 2018, Journal of Clinical and Diagnostic Research. All rights reserved.

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Journal of Clinical and Diagnostic Research, 2018, Oct, Vol-12(10): JC11-JC16 1111
DOI: 10.7860/JCDR/2018/36055.12109 Original Article
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Virtual Reality Therapy for Mental
Stress Reduction
INTRODUCTION
Mental stress can be regarded as the disturbance in the mental
balance of a person [1]. This is quite frequent in the case of students,
where changes in daily routine, peer pressure, employment, social
image and economic independence demand sudden increase in
responsibility, introducing a drastic change in lifestyle and hence
mental stress [2,3]. Students are seen to resort to dangerous
practices such as alcohol abuse and drug abuse when the level of
stress becomes very high [3]. If left unattended, stress can evolve
into other psychological problems such as depression, Post-
Traumatic Stress Disorder (PTSD), anxiety and suicide ideation [4].
Stress must be understood from the point-of-view of each
person to come up with effective and personalised solutions [3].
Questionnaires and also physiological data such as Respiratory
Rate (RR) and Electroencephalography (EEG) signals are used for
quantification of stress [5-8]. Therapy is required when the student
is unable to cope with stress on their own. Conventional methods
are quite cumbersome, with these forms of therapies exposing the
patients to life-threatening hazards [9,10]. Virtual Reality Therapy
(VRT) is one such form of therapy that is showing itself as a potential
workaround over these shortcomings. Virtual Reality, in short, is the
illusion of living in reality virtually [11]. It aims to inoculate behavioral
responses in the virtual world that are similar to those observed in
the real world [12]. Virtual Reality has enhanced ecological validity
due to its ability to mimic the real world nearly to perfection, greater
control over the stimulus given to the user by the therapist, thus
allowing for a better understanding of the effects, and also provide
stimulus that could be almost impossible or life-endangering to do
in the real world [12,13]. VRT also offers a high degree of immersion,
with even relatively less-immersive approaches such as flat screen
systems having enhanced ecological validity and effectiveness than
traditional approaches as long as it is able to mimic the real world and
respond well to user interaction [12]. Due to these advantages, VRT
is commonly being seen used in the domains of Stroke rehabilitation,
therapeutic application in Post-Traumatic Stress Disorders seen in
war veterans, balance disorders, pain alleviation for burn victims,
route learning of animals and humans, and also for understanding
brain functioning during these various tasks by coupling them
with techniques such as EEG and functional Magnetic Resonance
Imaging [12-14]. Although popularity is being gained among the
ranks of the medical community and an increase in the use of VR
solutions in research studies, it is still far from widespread adoption
due to the complex nature of the systems and their design and also
due to insufficient clinical evidence obtained [13].
This study is aimed at adding to this ever increasing list, creating
a simple, easy-to-use Virtual Reality Environment and testing it on
participants, studying the effect of VRT in rehabilitation of mental
stress using task performance metrics before and after therapy. This
study also utilises the conceptualisation capacity of EEG waves,
studying the variations in the mean band powers at various stages
of the therapy to better understand the brain responses to the same.
The various results are correlated to throw light on how effective
the therapy was at mitigating the stress present in the participants.
The study hypothesizes that Virtual Reality as rehabilitation aid will
reduce the stress for both controls and experimental group.
MATERIALS AND METHODS
Participant Selection: A total of sixty five final year students from the
department of Biomedical Engineering were engaged in this study.
The inclusion criteria were healthy, right-handed, and non-placed
participants in their final year undergoing campus placements and
want to work. This was due to the fact that there is an increased
amount of stress due to the pressure of getting a job and also due
to peer pressure. History of epileptic seizures was considered to be
VISHAL SUDHA BHAGAVATH ESWARAN1, MAHESH VEEZHINATHAN2,
GEETHANJALI BALASUBRAMANIAN3, ATUL TANEJA4
Keywords: Electroencephalography, Task performance, Stress, Virtual
ABSTRACT
Introduction: Mental stress is a perilous condition seen in
students that is often overlooked in the daily course of life.
With the advent of Virtual Reality Technology (VRT) and older
techniques being cumbersome and hazardous, Virtual Reality
Therapy is coming to the fore as a possible replacement in the
field of rehabilitation and psychiatry.
Aim: To understand the role of VRT and its effectiveness in
mitigating mental stress in students.
Materials and Methods: A sample size of 20 from a study popul-
ation of 65 healthy, right-handed, and non-placed participants in
their final year from the department of Biomedical Engineering were
separated equally into normal and mildly stressed groups based
on their DASS scores. A custom made Virtual Environment was
used for therapy and an experimental protocol was employed. The
Electroencephalogram (using RMS SuperSpec) and Mathematical
Go-NoGo Task Performance results before and after the therapy
were used to quantify the effectiveness of the therapy. The Mann-
Whitney U -Test was used for independent sample analysis of the
DASS scores between the Normal and Mildly Stressed Groups,
and the Post-hoc Wilcoxon Signed-Rank Test was used for related
sample analysis of EEG and Task Performance during Rest, Task
before therapy and Task after therapy.
Results: Though there was no significant difference in the
DASS scores between the two groups, there was a sense of
relaxation being imbibed into each group after therapy, by
virtue of increased mean alpha and decreased mean theta band
power in the EEG signals and also an increase in their task
performance after therapy.
Conclusion: Thus, this study shows the possible ability of VRT in
mitigating stress in the participants, and further studies between
various levels of stress and using various environments could help
establish VRT as a staple in the field of psychiatry for years to
come.

Vishal Sudha Bhagavath Eswaran et al., Virtual Reality Therapy for Mental Stress Reduction www.jcdr.net
Journal of Clinical and Diagnostic Research, 2018, Oct, Vol-12(10): JC11-JC16
1212
to the second stimulus and not equal to the same in the second type
of pair. The subject was tasked with pressing the response button (in
this study, the Enter button) as soon as possible for the first type pairs
and ignoring the second type pairs. For each pair, the inter-stimulus
interval was equal to 1100 ms, with the first stimulus lasting 400 ms
and the second stimulus lasting 200 ms. The duration between two
trials was equal to 3100 ms, with a total number of trials equal to
200 [17]. The parameters of performance were calculated for every
condition separately. This was realised using the Psytask software.
Virtual Environment: The Virtual environment screen was developed
to relieve the induced stress (by stroop task) during the experiment
protocol. The environment set the stage for a peaceful surrounding
wherein the user can relax and feel at peace.
The environment was created with the ability to stroll around in a
forest-like environment, interact with elements and also listen to
various natural sounds. This includes ability to walk or run around
the environment, using a horse as mount to navigate [Table/Fig-3],
colours that reflect nature, and also other human elements with
whom dialogue can be established. Brightness of the screen was
adjusted to the comfort of the user. To better improve the immersion
of the environment, a soundproof room was used and kept dark.
the exclusion criterion. The participants were filtered according to
these criteria and a total of 25 participants were selected. Based
on DASS-21 scores, these participants were segregated into two
groups, the score of 14 was considered as normal (control) and
the scores of 16 and above was measured as mildly stressed
(experimental) [15]. Two participants opted to discontinue from
the study while three participants displayed noisy EEG data, thus
bringing down the final number to 20, equally split into the two
groups (10 control + 10 experimental). The entire experiment was
conducted in a soundproof room where the participants sat in
comfortable seating arrangements. The experimental protocol was
carried out for duration of approximately 30 minutes in accordance
with the guidelines of the Institutional Ethics Committee for human
volunteer research. The experiment was conducted after obtaining
an informed consent from each of the participants, which gave them
complete details about the experiment, their role in the same, and
also guaranteed the safeguarding of their privacy, with the ability to
withdraw from the study whenever they wanted to. Both the groups
were exposed to Stroop Task to induce stress in the participants
during the experimental protocol of this study.
Stroop Task: The Stroop task consists of a set of words representing
a variety of colours. Each word is coloured in either the same or
different colour as the word. The user is tasked with describing the
colour of the word and not the word itself [16]. For example, the
word RED in the colour green must be described as GREEN and
not RED. The study used a set of five colours, and had two different
sets of stimuli. The congruent stimulus was the one where the word
and the colour inscribed were the same, whereas the incongruent
stimulus had one word and another colour. The task was created
as a computer application via Psycho Pi using Python. The triggers
were given via the computer screen and the participant responds
to the triggers using a set of keyboard buttons. [Table/Fig-1,2]
represent the Stroop incongruent (RED represented in Green colour)
and congruent (RED represented in Red colour) stimuli respectively.
The keyboard buttons used to respond were also depicted, with
the green colour representing the correct response key to the
corresponding stimulus.
[Table/Fig-1]: Diagrammatic representation of the stroop task incongruent stimu-
lus.
[Table/Fig-2]: Diagrammatic representation of the stroop task congruent stimulus.
Task Stimuli: The task stimuli used for this study was a mathematical
task Go-Nogo task. The visual stimuli were presented by pairs
corresponding to trials. The first stimulus is an arithmetical equation and
the second was an integer value. The first type of pair corresponded
to the result of the equation described by the first stimulus being equal
[Table/Fig-3]: Virtual environment.
System Specifications: The VR Environment was displayed
using a 15.6" laptop screen from a First Person Perspective. The
gaze movements in the environment were seen in response to the
movements in the mouse and interactions with various elements
in the environment were possible with the help of a keyboard. The
environment was created using the Unity 3D v5, a Gaming Engine
used to build high-graphic 3-Dimensional content using JavaScript.
The environment consisted of a Forest-like environment, with the
user able to move as they please and also interact with the various
elements present in it, for the entire duration of the therapy. The
therapy was given in a dark, sound-proof room and contained
ambient nature sounds to make the environment more immersive.
The Electroencephalograph (EEG) signals measured during the same
were recorded using the RMS SuperSpec with the 10-20 electrode
setup. Task performances given before and after the therapy were
mathematical tasks, deployed using the software PsyTask.
Experimental Protocol: At the start of the protocol, the participant
is made to sit with their eyes closed for obtaining baseline and
stabilising the signal. After three minutes of the same, the subject
is induced stress using the Stroop task, performing the task for
a total of three minutes. The subject is then made to perform
the mathematical task for a total of two minutes, immediately
after the induction of stress, thus allowing for a measure of their
performance during a stressed state. They are then subjected to
the Virtual Reality Environment for 10 minutes, allowing the subject
to calm themselves. The subject then performs the mathematical
task for another two minutes, measuring their performance during

www.jcdr.net Vishal Sudha Bhagavath Eswaran et al., Virtual Reality Therapy for Mental Stress Reduction
Journal of Clinical and Diagnostic Research, 2018, Oct, Vol-12(10): JC11-JC16 1313
a relaxed state. Finally, the subject goes back to closing their eyes
(baseline). The experimental protocol is depicted in [Table/Fig-4].
The experimental setup is depicted in [Table/Fig-5].
signal-to-noise ratio. A notch filter (Fc = 50Hz) was used to remove
any power-line interference. The filtered signal was then split into the
respective bands- theta (Band Pass Filter, Fc = 4-8Hz) and alpha
(Band Pass Filter, Fc = 8-13Hz). Beta bands were not taken into
consideration, as alpha waves and theta waves were more constant
in their changes to stressful conditions and thus better indicators
of stress [18,19]. Power Spectral Density (PSD) of the separated
bands were calculated and used for further statistical analysis.
The Mann-Whitney U-Test was used for independent sample
analysis of the DASS scores between the Normal and Mildly
Stressed Groups, and the Posthoc Wilcoxon Signed-Rank Test
was used for related sample analysis of EEG and Task Performance
during Rest (baseline), Task before therapy and Task after therapy.
The significance values (p) were measured with a threshold of 0.05.
The mean and standard errors of the significant samples were taken
for further graphical representation.
RESULTS
The experiment was conducted on a sample pool consisting of 65
final year non-placed students from the department of Biomedical
Engineering. A sample size of 25 was calculated with an error margin
of 15%, with two discontinuations and three noisy data giving a final
list of 20, which was segregated into two equal groups- control and
experimental.
In the Stroop Task, the percentage of correct answers for incongruent
stimuli was 95.87% for control and 92.87% for experimental group
while the percentage of wrong answers for control group was
4.1% and 7.3% for experimental group. The response time for
incongruent stimuli was 1225ms for the control group and 1596ms
for the experimental group.
The percentage of correct answers for congruent stimuli was 97.58%
for control group whereas its 97.24% for experimental group. While,
the percentage of wrong answers for control group was 2.42% and
2.76% for experimental group. For congruent stimuli, the response
time was 1120ms and 1394ms for the control and experimental
group respectively.
This is in line with the Stroop interference effect, with increased
response time in both groups for incongruent stimuli than congruent
stimuli [16]. This is further substantiated by the percentage of wrong
answers, both groups able to find it easier to answer congruent
than incongruent stimuli. The control group had increased correct
and decreased wrong answer percentages than experimental group
for both types of stimuli, suggesting an introduction of stress which
caused a reduction in performance. Normal group was able to cope
with the stress due to their inherent ability to do so, which was not
seen in the stressed participants [3].
As can be seen in [Table/Fig-7], there is no statistically significant
difference between the Normal and the Mildly Stressed Group (p>0.05).
The score of 14 was considered as Normal (control) and the scores
above 14 were measured as mildly stressed (experimental) [15].
For the normal group, there were statistically significant changes in
the mean Alpha Band Powers between Rest and Stressor (Stroop
Task) in the temporal region electrodes T4 (p=0.017), T5 (p=0.013)
and T6 (p=0.007) with a reduction in the mean band power
[Table/Fig-8a] and also between the tasks performed before and
after therapy in the temporal region electrodes T3 (p=0.013), T4
(p=0.007), T5 (p=0.013) and T6 (p=0.005) with an increase in mean
band power [Table/Fig-8b].
Statistically significant changes were also seen in the mean Theta
Band Powers across the temporal region electrodes T3 (p=0.009),
T4 (p=0.009), T5 (p=0.037) and T6 (p=0.028) between Rest and
Stressor (Stroop Task) with an increase in the mean band powers
[Table/Fig-8c] and in the temporal region electrode T6 (p=0.05)
between the tasks performed before and after therapy with a
decrease in the mean band powers [Table/Fig-8d].
[Table/Fig-4]: Experimental protocol.
[Table/Fig-5]: Experimental setup.
The EEG signals were recorded using the RMS SuperSpec
software. The standard 10-20 Electrode Setup was used for EEG,
with Ag/AgCl electrodes placed on the scalp in the prefrontal,
frontal, central, temporal, occipital and parietal regions. Reference
electrodes were placed in the earlobes and a ground electrode was
also used. The electrodes were coupled via a paste for impedance
matching. [Table/Fig-6] depicts the processing steps for EEG signals
diagrammatically.
The raw EEG signals (sampling frequency of 256Hz) obtained were
restricted to a band of 4-32Hz, with the delta waves (0.1-4Hz)
filtered using a band-pass filter (Fc = 4Hz-32Hz) to remove any eye
blink artifacts. The signal was passed through a moving average
filter using a triangular window (half-width of 30) to improve the
[Table/Fig-6]: EEG processing and feature extraction.

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[Table/Fig-7]: DASS scores-Control vs Experimental Group.
[Table/Fig-8a]:Mean Alpha Power for Control Group- Rest vs Stroop.
[Table/Fig-8b]: Mean Alpha Power for Normal Group-BVR vs AVR.
[Table/Fig-8c]: Mean Theta Power for Normal Group- Rest vs Stroop
[Table/Fig-8d]: Mean Theta Power for Normal Group- BVR vs AVR.
For the Mildly Stressed Group, there were statistically significant
changes in the mean Alpha Band Powers between Rest and Stressor
(Stroop Task) in the temporal region electrodes T3 (p=0.047), T4
(p=0.005), T5 (p=0.005) and T6 (p=0.005) with a reduction in
the mean band power [Table/Fig-9a] and in the temporal region
electrodes T3 (p=0.022) and T6 (p=0.009) between the tasks
performed before and after therapy with an increase in the mean
band power [Table/Fig-9b].
[Table/Fig-9a]: Mean Alpha Power for Mildly Stressed Group- Rest vs Stroop.
[Table/Fig-9b]: Mean Alpha Power for Mildly Stressed Group- BVR vs AVR.
Statistically significant changes were also seen in the mean Theta
Band Powers across the temporal region electrode T4 (p=0.037)
between Rest and Stressor (Stroop Task) with an increase in the
mean band power [Table/Fig-9c] but no statistically significant
changes were seen between the tasks performed before and after
therapy (p>0.05).

www.jcdr.net Vishal Sudha Bhagavath Eswaran et al., Virtual Reality Therapy for Mental Stress Reduction
Journal of Clinical and Diagnostic Research, 2018, Oct, Vol-12(10): JC11-JC16 1515
No statistically significant changes were seen between normal and
Mildly Stressed Groups.
In the Normal Group, there weren’t any statistically significant
difference in the Go Omission and No-Go Commission Errors (p>0.05)
[Table/Fig-10a]. This was in contrast to the Mildly Stressed Group,
which showed statistically significant changes in the Go Omission
(p=0.043) and No-Go Commission Errors (p=0.05, [Table/Fig-10b])
between the tasks performed before (BVR) and after therapy (AVR)
with a huge drop in these values. No statistically significant changes
were seen between normal and mildly stressed groups.
[Table/Fig-9c]: Mean Theta power for mildly stressed group- Rest vs Stroop.
[Table/Fig-10a]: Task Performance Metrics for Control Group-BVR vs AVR.
[Table/Fig-10b]: Task performance metrics for mildly stressed group-BVR vs AVR.
DISCUSSION
Virtual Reality-based rehabilitation therapy is widely used for stroke
recovery and an attempt is made to use this technique to reduce
the mental stress encountered in young adults. Based on the DASS
Scores, the participants are divided into control and experimental
group and the same VR environment employed. Thus, further
discussions will involve in analysing trends seen in each group. The
participants are positioned in a more naturalistic virtual environment
for safe navigation.
Task performance was improved in the Mildly Stressed Group after
therapy, with a decrease in their percentage of Omission for Go trials
and percentage of Commission for No-Go trials. This was in line with
the findings that Virtual Reality-based rehabilitation is seen to have
a positive impact, with studies done on Stroke, ADHD, Amnesia,
and TBI showing increased task performance after various sessions
of VRT [20]. The same cannot be said for the Control group, which
showed no statistically significant changes in these values. Normal
people can easily cope with stress and have a higher threshold than
stressed people [3]. This could possibly be the reason for there
being no change in the Task Performance metrics in the Control
group before and after therapy.
This trend in cognitive functioning can further be supported by the
results obtained from the EEG. When we consider the EEG band
powers of alpha and theta, a reduction in alpha band power and
an increase in the theta band power are correlated to an increase
in stress levels, with the converse also holding true [18,19]. This
is because Alpha waves are usually related to a state of calmness
while theta waves are usually related to stressful thinking and
disappointment [7]. When we consider the results obtained from
EEG, there is a significant reduction in the alpha band powers
and an increase in mean theta band powers for both the Normal
and Mildly Stressed Group from Rest to Stressor (Stroop Task),
indicating an increase in stress levels. While there was an increase
and decrease in the mean alpha band power and mean theta band
power respectively from the task performed before therapy to that
after therapy for the Control group, indicating the introduction of
calmness by the therapy in the participants, the Mildly Stressed
Group showed statistically significant change only in the mean
alpha band power from the task performed before therapy to that
after therapy. This could probably be due to the smaller sample
size considered. Furthermore, these changes are seen across the
electrodes T3, T4, T5, and T6 in the Temporal Region, which are
considered to relate to cognitive functions such as attention to
colour and shape, true and false memory recognition, visual fixation
and memory (Brodmann Areas 42, 21, 37) [19]. No changes were
observed in the rest of the lobes. Thus, the therapy was able to
improve cognitive functioning in the two groups, increasing their
Alpha Band Power, decreasing their Theta Band Power and hence
reflecting on their Task Performance results.
While intra-group variations in the parameters measured were
seen, no inter-group variation was seen in the study. This can be
attributed to the lack of any statistically significant change between
the DASS scores of the two groups. This could possibly indicate
the two groups not having too much of a variation in terms of their
stress levels. Though the study showed an improvement in cognitive
functioning and reduction in stress levels in each group, these
could not be compared between the two groups used in the study.
Thus no conclusive evidence regarding the relative extent of stress
reduction in the stressed group with respect to the control group
could be obtained. While one of the reasons could be due to the
overlapping of the DASS scores in both groups (as stated before)
and also the lower number of samples, another reason could be
due to the Virtual Environment not tailored to the preferences of
each participant. Thus, while the environment could be efficient
to certain users, it could be inconsequential in stress reduction in
others. Addition of an audio stimulus in the form of music could

Vishal Sudha Bhagavath Eswaran et al., Virtual Reality Therapy for Mental Stress Reduction www.jcdr.net
Journal of Clinical and Diagnostic Research, 2018, Oct, Vol-12(10): JC11-JC16
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PARTICULARS OF CONTRIBUTORS:
1. Student, Department of Biomedical Engineering, SSN College of Engineering, Chennai, Tamilnadu, India.
2. Associate Professor, Department of Biomedical Engineering, SSN College of Engineering, Kalavakkam, Tamilnadu, India.
3. Associate Professor, Department of Biomedical Engineering, SSN College of Engineering, Kalavakkam, Tamilnadu, India.
4. Student, Department of Biomedical Engineering, SSN College of Engineering, Chennai, Tamilnadu, India.
NAME, ADDRESS, E-MAIL ID OF THE CORRESPONDING AUTHOR:
Dr. Mahesh Veezhinathan,
Department of Biomedical Engineering, SSN College of Engineering, Chennai, Tamilnadu-603110, India.
E-mail: geethanjalib@ssn.edu.in
FINANCIAL OR OTHER COMPETING INTERESTS: None.
Date of Submission: Mar 02, 2018
Date of Peer Review: May 28, 2018
Date of Acceptance: Jul 23, 2018
Date of Publishing: Oct 01, 2018
also improve the efficiency of the VRT, making the environment an
audio-visual stimulus.
CONCLUSION
Results indicate a therapeutic effect introduced by Virtual Reality
Therapy in the Normal and Mildly Stressed Groups. The hypothesis
could not be proved i.e., inter-group variations were not seen
potentially due to the Normal and Mildly Stressed Groups not being
very different, due to the Virtual Environment being created not
considering preferences of the participants and also due to relatively
high margin of error (15%). By increasing the sample size (thus
decreasing margin of error), choosing Severely Stressed participants
instead of Mildly Stressed participants or creating a Virtual
Environment tailored to the users’ preferences with additional audio
stimuli, one can perform inter-group analysis of these parameters
and further substantiate the effects of VRT in stress reduction.
ACKNOWLEDGEMENT:
The authors of this paper would like to thank the Department of
Biomedical Engineering, SSN College of Engineering for providing
the necessary equipment and software used in this study.
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