Investigation of Electroencephalographic (EEG) Brainwave Signal on Mental Stress through Psychomotor Activities PDF Free Download
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Journal of Advanced Research in Applied Sciences and Engineering Technology 62, Issue 2 (2026) 245-258
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Journal of Advanced Research in Applied
Sciences and Engineering Technology
Journal homepage:
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index
ISSN: 2462-1943
Investigation of Electroencephalographic (EEG) Brainwave Signal
on Mental Stress through Psychomotor Activities
Alif Haiqal Khairul Shah1,2, Khairul Azlan A Rahman1, Thing Thing Goh1,*, Sin Jin Tan1, Christian Ritz3,
Norfaiza Fuad4
1
School of Engineering, University of Wollongong Malaysia, 4150 Shah Alam, Selangor, Malaysia
2
School of Engineering, UOW Malaysia KDU Penang University College, 10400 George Town, Pulau Pinang, Malaysia
3
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
4
Jabatan Kejuruteraan Elektronik, Fakulti Kejuruteraan Elektrik dan Elektronik, Universiti Tun Husseian Onn, Malaysia
ABSTRACT
Mental well-being plays a crucial aspect in human life alongside physical health. A
positive mental health allows individuals to engage in healthy relationship, navigate
challenges in a positive manner and enjoys a sense of fulfilment upon achieving goals.
Conversely, when mental health is not well taken care of, it can impair one’s ability to
function well in daily life, which subsequently leads to depression and anxiety disorder.
Hence, early detection and intervention is necessary for individuals experiencing
mental health challenges. The aim of this work is to study the recorded
Electroencephalogram (EEG) signals of human subjects for stress detection while
engaging in psychomotor activity. EEG signals captured from electrodes were sent to
OpenBCI on a laptop via a ganglion board for analysis. The data was then exported to
Matlab for further analysis using Fast Fourier Transform (FFT) and was monitored in
real time using Python. Volunteers between the ages of 18 and 50 were carefully
chosen, and the experimental conditions were closely monitored to reduce any
external factors that could affect stress levels and EEG readings. The findings reveal
notable changes in brainwave frequencies observed during periods of stress: a
decrease of 18.95% in alpha waves, a 9.89% increase in beta waves, and a 5% rise in
delta waves. Comparison was also carried out by placing the electrodes at different
location on the subject’s head Significantly, there was a 7.5% increase in alpha readings
at the top electrode positions, with beta (4.76%) and theta (1.93%) readings also
showing growth at the side positions on the head. An accuracy of 65% was obtained
using the developed monitoring system. This research effectively illuminates the
relationship between mental stress, physical activity, and EEG brainwave patterns. It
highlights the important role that brainwave frequencies play in influencing stress and
cognitive responses during physical activity. In addition, the advancement of real-time
monitoring systems offers dynamic tools for observing stress, improving opportunities
for personalized therapies and strategies for well-being.
Keywords:
EEG technology; stress response;
brainwave alterations; real-time
monitoring system
* Corresponding author.
E-mail address: ttgoh@uow.edu.my
https://doi.org/10.37934/araset.62.2.245258
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1. Introduction
Mental health problems related to stress have increased significantly and globally in recent years
[1,2]. These issues are impacting individuals across various sectors, especially in education. Anxiety
and depression among university students have become the worldwide concern as stated by the
paper [3]. Research carried out by Fuad et al., [4] shows an increase in anxiety and depression among
university students. Therefore, there is a need to investigate mental health growth, particularly with
the introduction of Electroencephalographic (EEG) sensors, which provide valuable information
about brainwave impulses.
An analysis of EEG brainwave patterns in conjunction with different physical activities has both
scientific and social significance in the context of the current fast-paced environment, where stress
is widespread. The complex correlation between human cognitive function and psychomotor activity
is a prominent area of study in the field of neuroscience. EEG technology enables the observation
and examination of brainwave patterns, providing insight into the impact of various psychomotor
activities on neurophysiological reactions to stress.
This study aims to explore how different psychomotor activities affect the neurophysiological
responses related to stress at different regions of brain using minimum number of electrodes. By
investigating the association between EEG brainwave patterns and mental stress, a complex
relationship between psychomotor activities and the brain’s stress response can be observed, with
their relationship proven by the studies done by [5]. The results of this study may provide new
approaches to enhance mental well-being by linking advanced stress-reduction methods with
neuroscientific investigation.
Brainwave types (Delta, Theta, Alpha, Beta and Gamma) show a reaction to stress even if they
respond differently, indicating changes from a rest state to a stress state. Particularly, Alpha, Delta
and Theta waves show significant reactions, which can be pivotal in detecting and addressing stress
early, which was conducted and concluded by [4,6,7].
The EEG device consists of electrodes, amplifiers, and a computer control module as explained in
the journal by Roy et al., [8]. It produces EEG graphs that illustrate brain activity while at rest. The
use of EEG sensors in different settings (wet and dry) revealed that dry EEG settings, although quicker,
tend to produce more noise compared to wet settings which provide higher quality signals due to
lower skin contact impedance, with the graphical output shown in Figure 1, extracted by the studies
from [9].
Fig. 1. Dry vs wet readings and its respective noise pollution [2]
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Electroencephalography (EEG) is a crucial non-invasive technique used to assess brain activity.
The method utilizes electrodes placed on the scalp to record electrical signals from the brain, allowing
for the examination of different mental states and neurological disorders. The versatility of EEG is
shown in its wide range of applications in clinical diagnostics, cognitive science, and brain-computer
interaction. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are
essential techniques for removing artefacts from EEG data such as signal distortion due to eye
movements, muscle activity etc, hence ensuring the accuracy as explained by [10].
Based on the study [11], brainwaves are classified into various categories, each linked to distinct
mental functions, and the study [12], explained that Delta waves are associated with profound sleep,
theta waves with relaxation and creativity, alpha waves with a state of calm alertness, beta waves
with active thinking, and gamma waves with advanced cognitive performance. These categorizations
facilitate the thorough examination of brain functionality and the advancement of neurofeedback
therapies.
Delta waves, which have a frequency between 0.5 and 3 Hz, are commonly linked to periods of
deep sleep and unconsciousness. Theta waves, which have a frequency range of 3 to 8 Hz, are
associated with states of relaxation, meditation, and creative visualization. Alpha waves range from
8 to 12 Hz, indicating a state of relaxed wakefulness and heightened awareness. Beta waves, which
typically range from 12 to 27 Hz, indicate a state of heightened mental focus, attentiveness, and
rational reasoning. Gamma waves, which have a frequency exceeding 27 Hz, are associated with
advanced cognitive processing, perception, and the integration of information, summarized in Table
1.
Table 1
Brainwaves signal categorized by frequency range [10]
Brainwave type (symbol)
Frequency (Hz)
Delta (δ)
0.5 – 3
Theta (θ)
3 – 8
Alpha (α)
8 – 12
Beta (β)
12 – 27
Gamma (γ)
> 27
Stress levels can be determined by analyzing EEG data concluded by [14], specifically by observing
brainwave patterns such as low alpha and high beta waves, which indicate increased stress levels.
This research presented a novel approach in detecting mental stress using only four frontal brain
electrodes. The objective is to reduce the complexity and cost of the system and at the same time
maintain the effectiveness of the result. By focusing on the reduced number of electrodes in the
frontal lobe region for mental assessment is highly effective. This area plays an important role in
regulating emotions and decision making, which directly connect to stress reactions.
Research done by [15] demonstrates the impact of physical and mental exercises, such as Qigong,
on brainwave responses. Qigong is a traditional self-cultivation exercise which coordinates body
posture and movements, meditation and mental focus to promote physical and mental well-being.
The EEG signals were obtained from multiple electrodes placed on the scalp to compare the signal
between mental and physical Qigong training. These activities can modify brainwaves such as theta
and alpha waves, and they also showcase the brain's ability to relax and cope with stress through
both physical and mental workouts [13].
Manipulating Emotions via Environmental Factors, Research has demonstrated that the presence
of certain sounds and visuals, such as soothing natural sounds and peaceful images, can successfully
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promote relaxation and alleviate stress, leading to improved mental and physical health, this was
concluded from the findings of [16] and [17].
Future Implications, the results from this study open doors to innovative methods for mental
health support, linking cutting-edge neuroscience with practical stress-reduction strategies. The
development of a real-time monitoring system for brainwaves could potentially enable early
warnings for rising stress levels, contributing significantly to preventive mental health care.
This research will focus on utilizing a minimum number of electrodes placed at the forehead, side
and top (frontal brain region) to detect stress during psychomotor activities aiming to identify the
best positions for EEG brain wave detection. It is to offers the promise of personalized mental health
interventions tailored to individual needs, ultimately benefiting mental health outcomes.
2. Methodology
2.1 Test Subjects
The investigation into mental stress and EEG brainwave signals prioritized the careful selection of
test subjects to ensure result accuracy and consistency. Criteria for inclusion were formulated to
establish a uniform sample for meaningful outcomes. Participants, aged 18 to 50, were chosen to
represent individuals familiar with academic and professional stressors in a university setting,
focusing on the University of Wollongong Malaysia community to ensure inclusivity. A total of 30
participants will be recruited for the study. Rigorous screening excluded individuals with neurological
or psychological disorders or those using medications affecting neurological or psychological
functioning, aiming to create a sample of neurotypical individuals unaffected by medical conditions
that could influence outcomes. Ethical considerations, including informed consent and participant
well-being, were central to the screening process.
2.2 Experiment
The study sought to establish an ideal setting for examining the correlation between mental stress
and EEG brainwave signals while engaging in physical activities. By referring to [32], researchers have
suggested five sessions of study protocol including Training, Rest State, Control State, Stress State
and Mitigation State. In order for the experiment to be carried out with minimal disruptions and
participant comfort, the sessions took place in a quiet, soundproof, and temperature-controlled
environment. Relaxation was induced and a stable experimental condition was maintained by
utilizing nature movies and green noise.
In order to produce psychological stress, the participants were assigned the job of manipulating
objects on a table, which involved making strategic decisions and placing a higher cognitive burden
on them. The intentional task design was intended to establish a regulated yet challenging setting for
the observation of stress-induced EEG patterns during physical activities.
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2.2.1 Procedure
Fig. 2. Flowchart of the procedure for the stress
experiment
The flowchart depicted in Figure 2 outlines the step-by-step procedure of the stress experiment.
The experiment begins with participants wearing headwear equipped with electrodes to measure
EEG brainwave signals. This is followed by a 5-minute visual and audio simulation designed to induce
mental stress. Participants then proceed through a series of psychomotor activities involving the
movement of numbered balls between designated positions, with intermittent waiting periods. The
process is repeated until all tasks are completed, allowing for continuous monitoring and analysis of
EEG signals in response to stress-inducing activities.
2.3 Electrode Placement
The 10-20 EEG strategy has been identified by [18,19] and [23] as a successful technique for
positioning electrodes in electroencephalography studies. This standardized procedure entails
dividing the distance between anatomical landmarks into specific segments, guaranteeing consistent
and accurate insertion of electrodes across individuals. Through the utilization of the 10-20 approach,
researchers can reliably position electrodes on certain areas of the brain, which aids in obtaining
dependable EEG recordings and allows for accurate evaluations of brain function.
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Hence, the two settings in terms of position of electrodes based on the 10-20 used in this are
the Cython, which places the electrodes on the forehead and top of the head and the Ganglion,
which places the electrodes on the forehead and the sides of the head.
Fig. 3. 10-20 EEG vs 10-10 EEG node placements
The complexity of node placements is shown in Figure 3. The 10-20 EEG system was used due to
its practicality and uniformity in node selection. Although the system has a bigger node size, which
should imply worse accuracy, it provides more consistent results. The reason for this is that larger
nodes facilitate the selection of the correct node, hence ensuring enhanced precision in electrode
insertion. The 10-20 EEG system is a dependable option for EEG investigations due to its user-friendly
design and consistent outcomes, despite its greater node size.
This study focuses on collecting EEG signals from only six frontal electrodes (FP1, FP2, F7, F8, F3
and F4) to identify the best positions for collecting EEG signal in stress assessment, refer to Table 2
and Figure 4. The 10-20 EEG system is widely used due to its ease of use and reliable results. This
study focuses on six frontal electrodes (FP1, FP2, F7, F8, F3, and F4) because the frontal lobe,
particularly the prefrontal cortex, plays a crucial role in emotional regulation and stress response.
Previous research has shown that stress-induced changes in brainwave patterns are most
prominently detected in these regions, making them ideal for stress assessment [24].
Table 2
Brain area focused and its respective node
Area
10-20 EEG Node
Forehead
FP1 & FP2
Side
F7 & F8
Top
F3 & F4
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Fig. 4 Electrode placement for top (Cython) and side (Ganglion)
2.4 Microcontroller Board
The diagram in Figure 5, Figure 6 and Figure 7 displays all the necessary connections required for
the entire system to function. The process starts by positioning electrodes strategically on the
subject's scalp. The reference and ground wires are positioned on the left and right earlobes,
respectively. The process starts by positioning electrodes strategically on the subject's scalp
according to standardized systems like the 10-20 system, ensuring consistent and accurate
measurements of brain activity. The reference and ground wires are positioned on the left and right
earlobes, respectively, to minimize electrical noise and interference, thus improving the signal quality
and reliability of the EEG recordings [24,25]. Subsequently, a lithium-ion battery with a voltage of
3.5V is employed to energize the ganglion board, which serves to interconnect the electrodes,
reference wire, and ground wire. The ganglion board thereafter communicates the received and
interpreted data to the laptop, referred to as the central hub, through the 2.4GHz dongle, which is
connected to the laptop.
Fig. 5. Connection of circuit
/ Cython
Board
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Fig. 6. Head strap, 2.4Hz Dongle,
Reference, and ground cables
Fig. 7. Electrodes and ganglion board
2.5 Data Collection and Analysis
Open Brain-Computer Interface (OpenBCI), shown in Figure 8, is a reliable platform that enables
smooth data analysis for EEG, making it easier to go from recording to interpreting raw data. It
gathers raw brain electrical signals using attached electrodes, distinguishing different brain waves
such as beta, theta, delta, and gamma, offering a comprehensive understanding of brain activity. The
Fast Fourier Transform (FFT) plays a crucial role in OpenBCI's data processing, efficiently converting
EEG signals from the time domain to the frequency domain. This transformation allows for spectrum
analysis, which helps researchers and developers visualize the distribution of energy across specific
frequency ranges. This can help identify patterns related to various cognitive states or tasks. In
addition, OpenBCI enables individual electrode tracking, which is essential for spatial analysis to
precisely identify active brain areas or responses to stimuli.
The Fast Fourier Transform (FFT) is integral to OpenBCI's data processing, as it efficiently converts
EEG signals from the time domain to the frequency domain [26]. FFT's role in converting EEG signals
into the frequency domain is crucial because it simplifies the analysis of complex brain wave data,
making it easier to detect changes and patterns that correspond to different mental states or
responses to stimuli [27]. Additionally, OpenBCI supports individual electrode tracking, which is
essential for spatial analysis. This feature allows for precise identification of active brain areas or
specific responses to stimuli, enhancing the accuracy and granularity of EEG-based research [28].
In addition, OpenBCI allows for easy data export, making it simple to convert processed data into
formats like CSV files. The files are structured to arrange data based on specific dates. Each row
includes raw data, band power values for various frequency bands, and details about electrode
activity. OpenBCI has a data sampling rate of 200 Hz, which allows for precise temporal resolution.
This is crucial for accurately capturing the dynamic fluctuations in brain activity as they occur over
time. This export function improves compatibility with different analytic tools and applications,
making it easier to integrate EEG data into various study or application environments.
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Fig. 8. OpenBCI user interface
The output data is divided into segments based on the raw signal, allowing each electrode reading
to be accessed independently for further analysis. This helps to make the dataset more manageable
for processing in MATLAB. This segmentation is crucial because of the use of four electrodes, each
transmitting a distinct electrical signal. It enables the differentiation and evaluation of the individual
contributions of each electrode to overall brain function or cognitive processes. After segmenting the
data, an analysis using Fast Fourier Transform (FFT) is performed on the waveforms from each
electrode to break them down into different frequencies. Frequency domain analysis allows for the
extraction of frequency information, which helps identify and characterize neurological oscillations
in different frequency bands. This analysis provides valuable insights into the dynamics of brain
activity and patterns that indicate various cognitive processes or states. The FFT output of individual
electrode and the plug in FFT analyzer are shown in Figure 9.
Fig. 9. Individual electrode FFT and the FFT analyser
The analogue form of each electrode signal is first received to ensure continuous data
representation without the requirement of digital conversion. This selection is crucial in maintaining
the integrity of the raw signals, guaranteeing that no information is lost during the acquisition
process. Preserving the analogue structure of the signals allows for precise preservation of minor
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subtleties and oscillations in the EEG data, providing a thorough understanding of brain function.
After receiving the signals, the raw data from each electrode is meticulously transformed into a
MicroVolt range that ranges from 0uV to 100uV. The conversion method serves to standardize the
signals, making it easier to conduct consistent analysis across many electrodes and sessions.
Following the process of standardization, the subsequent essential step entails the application of
the Fast Fourier Transform (FFT) to the signals. This mathematical technique allows for the analysis
of EEG signals by breaking them down into individual frequency components, revealing the
distribution of frequencies present in the brainwave data. This method has been previously utilized
[20].
By utilizing specialized plug-ins, the FFT analysis explores the data in greater detail, carefully
examining variations and patterns that may otherwise be overlooked. Subsequently, the mean Fast
Fourier Transform (FFT) value is calculated for each electrode, resulting in a unified measure of
brainwave activity. The average values are divided into specific frequency ranges that correlate to
unique brainwave frequencies, enabling focused investigation of alpha, beta, theta, and delta waves.
Ultimately, the mean FFT values from each electrode are combined and averaged again, resulting in
a full summary of brainwave activity over the whole array of EEG recordings.
2.6 Real-time Monitoring System
A real-time stress monitoring system has been developed using Python in this research, starting
with a 5-minute relaxation session to establish a baseline calm state for each user utilizing Python
and MATLAB for advanced numerical computation and data analysis. It only flags stress when certain
conditions are satisfied, such as a simultaneous decrease of 19% in alpha activity, a 10% increase in
beta activity, and a 5% change in theta activity. This method is grounded in extensive research
indicating that specific patterns of brainwave changes are associated with stress [29,30].
This Python and MATLAB integration enables a thorough understanding of users' mental well-
being, enabling timely interventions to enhance resilience and overall mental health. The use of these
software tools enables detailed analysis and visualization of EEG data, facilitating timely interventions
to enhance mental well-being by identifying and mitigating stressors effectively [31].
3. Results
3.1 Reaction of Alpha, Beta and Delta
The research carried out by [19,21,22] offers useful insights into the regulation of brainwave
frequencies in response to stress induced by physical activity. Although precise numerical numbers
are rarely specified, all investigations consistently demonstrate changes in brainwave patterns.
Nabilah et al., [19] research indicates a notable decline in alpha waves, which suggests a drop in
relaxation. On the other hand, [21] study shows a decrease in beta waves, indicating an increase in
cognitive alertness. Similarly, [22] research demonstrates a decrease in theta waves, indicating a
transition away from reflective cognitive processes.
Collectively, these investigations indicate a synchronized neurophysiological reaction to stress
triggered by physical exertion. Figure 10 presumably shows a consistent pattern of alpha wave
decrease, beta wave increase, and theta wave decrease during stress caused by physical activity. This
pattern is based on data collected from three different studies. This synthesis enhances
comprehension of the neurological systems that govern the stress response during physical activity,
hence aiding the advancement of efficient stress management approaches.
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Fig. 10. Results of brainwave signals (Alpha, Beta and Theta) going from relaxed to
stress
3.2 Difference in Position of Electrodes
The objective of the study and the characteristics of stress being studied will determine the
placement of electrodes. This highlights the importance of identifying the appropriate electrode
locations to thoroughly understand the neurological aspects of stress, in a clear and professional
manner.
The readings are nevertheless impacted by the electrode locations. When comparing just the
stressed readings, better alpha reading of 7.5% is displayed in the top reading when the user is in the
Cython configuration. Better readings of beta and theta readings, at 4.76% and 1.93%, respectively,
can be seen on the side reading while the user is in ganglion setting, which the graphical format can
be seen in Figure 11.
Fig. 11. Results of brainwave signals (Alpha, Beta and Theta) based on four electrodes
The studies by [19,21,22] offer crucial insights into how brainwave patterns are regulated in
response to physical stress. From our findings, derived from a study with 35 subjects, not only
corroborate their results but also deepen the understanding of neurophysiological responses to
stress. Nabilah et al., [19], who worked with 30 subjects, observed a significant decrease in alpha
waves associated with relaxation. Our research further confirmed this by showing a 7.5% better alpha
50
55
60
65
70
75
80
85
90
95
1357911 13 15 17 19 21 23 25 27 29
Amplitude (uV)
Participant Number
Alpha, Beta and Theta Brainwave signals during experiment
Alpha Relaxed
Alpha Stressed
Beta Relaxed
Beta Stressed
Theta Relaxed
Theta Stressed
50
55
60
65
70
75
80
85
90
95
12345678910
Amplitude (uV)
Test Subject No.
Alpha, Beta and Theta (top and Side position)
Ganglion Alpha
Cython Alpha
Ganglion Beta
Cython Beta
Ganglion Theta
Cython Theta
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reading. The research by [21], which involved 25 subjects, identified a reduction in beta waves linked
to cognitive alertness, consistent with our finding of a 4.76% improvement in beta readings in the
Ganglion configuration. Similarly, [22] found a decrease in theta waves (reflective processes) in a
sample of 28 subjects, aligning with our 1.93% better theta reading. The larger sample size in our
study enhances the validity and reliability of these observations, highlighting the importance of
electrode placement in accurately measuring brainwave activity and advancing the development of
effective stress management strategies.
3.3 Accuracy of Real-Time Monitoring System
Talha, Eissa, and Shapiai [20] present a novel approach to real-time deep learning-based stress
detection by combining voice, facial expressions, and ECG signals. This multimodal technique
successfully detects stress, demonstrating its potential for useful mental health monitoring and
intervention with an accuracy rate of 85.1%. By maximizing different neural network topologies, [18]
provides a hybrid deep learning model for stress detection using EEG analysis that achieves a
classification accuracy of 97.60%. The use of 14 channels by this model provides a thorough
understanding of brain activity, improving its accuracy in recognizing stress patterns. In comparison,
our study used only 4 channels and achieved a 65% accuracy in detecting stress from brainwave
signals. Despite fewer channels, our results confirm the potential of brainwave signals as reliable
stress indicators. These findings, along with the higher accuracy of more complex models, highlight
the need for advanced techniques and multimodal methods to improve stress detection for better
mental health monitoring and intervention.
4. Conclusions
In conclusion, this study effectively accomplishes its goals by carefully analyzing how various
physical activities, mental stress, and EEG brainwave signals—with a particular emphasis on theta,
beta, and alpha waves—interact. Results show that during stress, alpha waves significantly decreased
by 18.95%, whereas beta waves increased by 9.89% and theta waves increased by 5%. Notably, side
positions display increases of 4.76% in beta waves and 1.93% in theta waves, whilst top readings
indicate an increase of 7.5% in alpha waves. The study investigates correlations between stress,
psychomotor activity, and brainwave signals in addition to characterizing different types of
brainwaves and the best places to assess them. It clarifies how these waves respond differently to
physical strain and stresses, which is important information for comprehending how stress is
regulated and how the mind reacts to different kinds of activity. It also creates a real-time stress
detection system that uses delta, beta, and alpha signals. This system offers dynamic tools for
ongoing monitoring and customized therapies. Based only on brainwave measurements, the
technology shows efficiency in stress diagnosis and treatment with an accuracy of 65%.
In future, this research can be enhanced by combining facial expressions, body temperature
detection and machine learning to enhance the accuracy in stress diagnosis.
Acknowledgement
This research was funded by Fundamental Research Grant from Ministry of Higher Education
Malaysia (MOHE) with the reference number of FRGS/1/2023/TK07/KDU/02/1 and UOW Malaysia
KDU Research Grant with reference number of UOWMKDURG/2023/005.
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