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A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.
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Introduction to the identification of brain waves based on their
Zuzana Koudelková1,*, Martin Strmiska1
1Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, 76005 Zlin, Czech Republic
Abstract. A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper,
the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram
(EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain
wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and
applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves
by their frequency. The first type of the measurements was based on logical and analytical reasoning, which
was captured during solving mathematical exercise. The second type was based on relax mind during listening
three types of relaxing music. The results of the measurements were displayed as a visualization of a brain
1 Introduction
The nervous system is composed of two parts. The central
nervous system (CNS) and the peripheral nervous system
(PNS). The CNS consists of human brain and spinal cord.
Conversely, the PNS consists of the nerves and ganglia
outside the brain and spinal cord.
Human brain controls body function, such as heart
activity, movement, speech, but also thinking itself,
memory or emotion perception. Brain activity could be
measured by the neurologic examination method
electroencephalography (EEG). The principle of this
method is capturing electric potential.
If the central nervous system is damaged, some body
functions may be restricted. Brain computer interface
systems could offer these people improved
communication and independence.
Recent developments in BCI technology may see such
hands-free control method realised. A BCI is a
communication and control system in which the thoughts
of the human mind are translated into real-world
interaction without the use of the usual neural pathways
and muscles. For example, users of BCI system can
switch a light or change TV channels using only their
imagination and without any physical movement. Recent
advances in the human brain and BCI research reveal that
BCI-based devices and technologies can play a significant
role in the future [1-3].
The article begins with theoretical information about
BCI and its types. After that, there is mentioned signal
acquisition, where the brain wave distribution is described
by frequency. The next topic of this article are methods
and measurements. There are described device and
application, which have been used. Also, there is defined
using types of measurements. The following is the Results
section with the measurement results, which are displayed
as visualization of brain activity.
2 Brain Computer Interface
Brain Computer Interface acquire and analyse brain
signals in real time to control external devices,
communicate with others, facilitate rehabilitation or
restore functions.
There are three parts of Brain Computer Interfaces.
Invasive, Non-invasive and Partially invasive.
2.1. Invasive brain computer interface
Invasive Brain Computer Interface systems are used for
the best quality signals. These electrodes are implemented
into cortical issue. These types of system are used for
paralyzed people, or it could be used for restoring vision
by connecting the brain with external cameras. Although
these BCI systems provides the best quality signals, the
system is prone to scar-tissue build-up. The scar-tissue
cause weak signal, which can be even lost. Because of the
body reacts to a foreign object in the brain [2].
2.2 Partially invasive brain computer interface
Partially Invasive Brain Computer Interfaces are
implanted into the skull, but outside the brain.
Electrocorticographic (ECoG) uses the same technology
as non-invasive electroencephalography, but the
electrodes are embedded in a thin plastic pad that is placed
above the cortex, beneath the dura mater. These systems
produce a good signal, but weaker than Invasive BCI [2].
MATEC Web of Conferences 210, 05012 (2018)
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2.3 Non-Invasive brain computer interface
Non-Invasive Brain Computer Interfaces means
electrodes are emplaced on the surface of the skull to
record changes in EEG state. The signal, which is
producing has the weakest values in spite of this, the non-
invasive BCI are the safest and easiest way to record EEG
[2]. The schema of brain computer interface system is
shown in Figure 1.
Fig. 1. Basic flow diagram of BCI system.
3 Signal Acquisition
EEG is the most prevalent method of signal acquisition
for Brain Computer Interfaces. Many EEG systems are
using International 10/20 system. This is an electrode
placement strategy, which ensures ample coverage over
all parts of head [4].
Brain signals are acquired by electrodes on the surface
of the head. Then these signals are digitized and processed
to clean and denoise data to enhance the relevant
information embedded in the signals. After that, step
called the feature extraction is used. It means that the brain
patterns used in BCIs are characterized by certain
features. Describing the signals by a few relevant values
is called feature extraction. Next step is translation. This
step assigns a class to a set of features extracted from the
signals. This class corresponds to the type of mental states
identified. Finally, the translation into a command, which
means a command is associated to acquired mental state
in order to control application.
The most important criteria of evaluation EEG are
frequency. This is a criterion for assessing abnormalities
in clinical EEG and for understanding functional
behaviours in cognitive research [5].
In Table 1 there can be seen five major brain waves
distinguished by their different frequency ranges (Figure
Table 1. Brain wave distribution by frequency of band
Name of the
Frequency of band wave
alpha α 8-13
beta β 13-30
delta δ 0.5-4
gamma γ >30
theta θ 4-8
Each brain wave has different frequency, amplitude
(Figure 2) and meaning. Alpha α waves connect the gap
between our conscious thinking and subconscious mind.
It helps us to calm down or it promotes a feeling of
relaxation. Beta β waves are active in a waking state. This
frequency is visible in logical-analytical reasoning. In
their activity, we focus on a problem solving. Delta δ
waves occur during meditation in a state of deep sleep or
coma. Abnormal delta activity may occur with the person,
has learning disabilities or have difficulties maintaining
conscious awareness (such as in cases of brain injuries).
Gamma γ waves are important for learning, memory and
information processing. Theta θ waves are involved in
sleep or daydreaming. This brain wave can indicate
intuition or automatic tasks [6].
Fig. 2. Five major brain waves distinguished by their different
frequency ranges [5].
4 Methods and Measurements
The problem of using BCI in the academic field is a high
price. Best option for academic purposes is using the
Emotiv EPOC device (Figure 3), which is designed and
manufactured by Emotiv System. The device comprises a
wireless helmet that enables the reading of feelings,
emotions, and intentions of the user. The cost of this
device is 799$ for a Research Development Kit.
MATEC Web of Conferences 210, 05012 (2018)
CSCC 2018
Fig. 3. Wireless helmet Emotiv EPOC.
The Emotiv EPOC device is based on International
10/20 system. This headset consists of the 16 sensors on
the scalp. Two of these sensors are references. The set-up
of this device is fast, but there is a problem with
connection of the sensors.
Measurement takes place in application Emotiv Brain
Activity Map v3.3.3 (Figure 4), which is developed by
Emotiv Systems. The cost of this application is 9.95$. The
application measure and display real-time data of four
types of brain waves. Alpha, Beta, Theta and Delta. Each
of these frequencies allows adjustable gain to see detailed
information and relative strengths between different brain
regions. Adjustable buffer size allows to see instant
responses or average activity over longer periods.
Fig. 4. Application Emotiv Brain Activity Map v3.3.3
developed by Emotiv Systems.
In this paper, there were used two types of
measurement. The first type of measurement where
analysis brain activity, which is produced by logical and
analytical reasoning. The measurement was performed
during solving mathematical exercise.
The following type of measurement was based on
relaxed mind. Measurements were taken in a quiet room
with a relaxing music. Three types of relaxation music
were used in this paper.
5 Results
Results are taken as a visualization of brain activity. The
colour of the weakest signal is blue. As the strength of
signal increases, the colour changes to red. This is shown
in Figure 5.
Fig. 5. Spectrum of signal colours from the weakest to the
5.1. The measurement based on logical and
analytical reasoning
In the first measurement, the person used for the
measurement, was given a mathematical exercise with the
command to solve it.
At the beginning of the measurement it was detected
that the main roles play beta and theta waves. It was
proved that beta waves occur when solving a problem or
they are visible at logical-analytical reasoning. It can be
seen in Figure 6.
Fig. 6. Brain activity at the beginning of the measurement of the
problem solving.
After one minute, the view has changed. Now, there are
active only theta waves. It is because the testing person
solved the exercise in the past. This was explained that
theta waves occurred during the automatic problems
solving. This can be seen in Figure 7.
Fig. 7. Activation of theta brain waves.
5.2. The measurement based on relaxed mind
The second measurement was concerned with the relaxing
mind. The person used for the experiment was taken a seat
with a quiet room with the command to close their eyes
and relax. After that, the relaxing music was played.
Relaxing music should activate alpha waves. In this
MATEC Web of Conferences 210, 05012 (2018)
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experiment, it is used three types of relaxing music. Each
of that proved it. It can be seen in Figure 8 and Figure. 9.
Fig. 8. Brain activity during the first type of listening relaxing
Fig. 9. Brain activity during the second type of listening
relaxing music.
The last of the relaxing music also actives theta waves.
Explaining the presence of theta waves is likely to be the
beginning of deep relaxation. This is shown in Figure 10.
Fig. 10. Brain activity during the third type of listening relaxing
6 Conclusion
This paper briefly described what is Brain Computer
Interface and its types. After that, electroencephalogram
has been described. EEG is a non-invasive method can
also be used for academic purposes. The measurement
was mainly concerned with the frequency of brain waves.
Brain waves, that occur during problem solving or
relaxation, has been measured by the application Emotiv
Brain Activity Map.
Our research deals with BCI system, which was
identified by brain waves in two different actions. Firstly,
we managed to measure data while the person solving a
problem. Secondly, we measured the person while
relaxing. There were used the type of relaxing music.
After that, we evaluated and described the collected data.
This measurement is the beginning of the further research.
Future work lies primarily with the purchase of PRO
license that will enable the raw EEG signal to be
processed further. The RAW EEG signal can also be
processed in other applications instead of Emotiv
applications. This type of the measurement could be taken
by people with epilepsy or other abnormalities in clinical
EEG. BCI technology is a relatively new research area
with great application potential. This is mainly a possible
improvement in the quality of life for patients with
permanent neurological deficits. By implementing this
method into neuro-rehabilitation practice, we can improve
the patient's health and mental state.
This work was supported by Internal Grant Agency of Tomas
Bata University in Zlin under the project No.
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... These signals captured through electrodes on the scalp's surface are measured as voltage against actions. These signals are then classified between free-running, hybrid, and evoked components (Koudelková & Strmiska, 2018). Delta waves are low-frequency waves observed when deeply relaxed individuals, such as deep sleep or deep meditation. ...
... During rapid brain activity, gamma waves may occur. Gamma waves are often associated with the brain, which requires attention and perception of cognitive and behavioural processes (Koudelková & Strmiska, 2018;Seal et al., 2020). ...
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The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.
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In the last fifteen years, a recognizable surge in the field of Brain Computer Interface (BCI) research and development has emerged. This emergence has sprung from a variety of factors. For one, inexpensive computer hardware and software is now available and can support the complex high-speed analyses of brain activity that is essential is BCI. Another factor is the greater understanding of the central nervous system, including the abundance of new information on the nature and functional correlates of brain signals and improved methods for recording these signals in both the short-term and long-term. And the third, and perhaps most significant factor, is the new recognition of the needs and abilities of people disabled by disorders such as cerebral palsy, spinal cord injury, stroke, amyotrophic lateral sclerosis (ALS), multiple sclerosis, and muscular dystrophies. The severely disabled are now able to live for many years and even those with severely limited voluntary muscle control can now be given the most basic means of communication and control because of the recent advances in the technology, research, and applications of BCI.
  • H S Anupama
  • N K Cauvery
  • G M Lingaraju
H.S. Anupama, N.K. Cauvery, G.M. Lingaraju, International Journal of Advances in Engineering & Technology, 3, 739-745 (2012)
Identification of some brain waves signal and applications
  • N Thi
  • H Hanh
  • H Van Tuan
N. Thi, H. Hanh, H. Van Tuan, Identification of some brain waves signal and applications, ICIAE 12, 1415-1420 (2017)
  • S Xie
  • W Meng
S. Xie, W. Meng, Biomechatronics in medical rehabilitation, (New York, NY: Springer Berlin Heidelberg, 2017), ISBN 9783319528830