EEG ANALYSIS OF DRUG
USERS WITH WAVELET
METHODS
Daniel Septry Panjaitan
1.14.4.085
`Jika Kamu tidak dapat menahan lelahnya belajar,
Maka kamu harus sanggup menahan perihnya Kebodohan.'
Imam Sya 'i
Acknowledgements
All praise and gratitude writer are turning to the presence of God Almighty
because with His bounties and blessings I can nish rst Internship report
titled "Analysis of EEG Against Drug Users Using WAVELET Method"
which is one of the requirements to continue the learning process to the
next level.
In writing this, I Internship report writers face many obstacles, one of
which is the di culty in obtaining data and information as well as the
limitations of the knowledge possessed by the author. However, the au-
thors attempted with the capabilities to complete this report.
For that on this occasion the author would like to thank:
1. To Parents, Brothers, and All my friends who have supported both
regarding morale to the material.
2. Both my parents and family have encouraged and encouraged me.
3. Rolly Maulana Awangga, ST, MT as lecturers on campus and in the
Internship which has provided guidance as well as ease of analysis
and preparation of reports.
4. All those who have contributed to the completion of this rst Intern-
ship report.
5. Syafrial Fachri Pane, S.T., M.T.I. As the Companion Examiner for
this Internship I stage.
6. Roni Andarsyah. S.T., M.Kom. As Internship I Coordinator for
Academic Year 2018/2019.
7. M. Yusril Helmi Setyawan, S.Kom., M.Kom. As Chair of the 2018/2019
DIV Informatics Engineering Study Program.
The author realized that writing this report is far from perfect, therefore,
criticism and constructive suggestions are needed by the author to work
more leverage for the future.
End the authors hope that this report can be useful and add a better
insight to the reader.
Bandung, december 11, 2018
Abstract
Electroensephalogram (EEG) is an activity to record the electrical activity
of brain neurons. EEG is often used to analyze brain activity and predict
the emotions produced, by using EEG relaxed conditions of drug users
can be observed. EEG signals are widely used to detect brain disorders in
the health world. However, the signal produced by the EEG needs to be
prepared for the process to be able to detect brain abnormalities automat-
ically. Therefore, there is a need for a preprocessing method to produce
the right features in order to obtain precisely and accurately stored char-
acteristics of the EEG signal. This research will be developed using the
Loreta method. Therefore, the researcher will design portable devices and
application systems that can monitor the condition of the brain using EEG
sensors correctly.
Contents
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problems Identi cation . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Objective and Contribution . . . . . . . . . . . . . . . . . . . . . . . 2
1.3.1 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3.2 Contributione . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Scoop and Environtment . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Related Works 3
2.1 Electroencephalogram . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2.1 Brain Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.3 MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.4 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Case Study 6
3.1 History of case study . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 Vision and Mission of case study . . . . . . . . . . . . . . . . . . . . 6
3.2.1 Company vision . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2.2 Company mission . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.3 Organization Structure of case study . . . . . . . . . . . . . . . . . . 7
3.3.1 Organization Structure of case study . . . . . . . . . . . . . . 7
3.3.2 Job Description of case study . . . . . . . . . . . . . . . . . . 7
3.4 Data description source . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.4.1 Description Participants Internship Job Type I . . . . . . . . . 7
3.4.2 Scope Internship I . . . . . . . . . . . . . . . . . . . . . . . . 8
3.4.3 Responsibilities of Participants Internship I . . . . . . . . . . 8
3.4.4 Participants Job Description Internship How Far I . . . . . . . 8
4 Methods 9
4.1 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2 Method Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
5 Experiment and Result 11
5.1 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.1.1 Tools Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5.1.1.1 EEG ampli er Mitsar -202 and WinEEG . . . . . . . 11
5.1.1.2 Electro-Cap . . . . . . . . . . . . . . . . . . . . . . 12
5.1.1.3 MATLAB . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2.1 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2.1.1 raw Data . . . . . . . . . . . . . . . . . . . . . . . . 13
i
5.2.1.2 bandpass Filter . . . . . . . . . . . . . . . . . . . . . 13
5.2.2 Data Processing Using Wavelet . . . . . . . . . . . . . . . . . 14
5.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6 Conclusion 16
6.1 Conclussion of Problems . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.2 Conclusion of Method . . . . . . . . . . . . . . . . . . . . . . . . . . 16
6.3 Conclusion of Experiment . . . . . . . . . . . . . . . . . . . . . . . . 16
7 Discussion 17
Bibliography 18
List of Figures
2.1 Form biolistic Brain Signals . . . . . . . . . . . . . . . . . . . . . . . 4
3.1 The company logo . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 IRC Organizational Structure . . . . . . . . . . . . . . . . . . . . . . 7
4.1 Flowchart research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2 Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.1 EEG ampli er system Mitsar-202 . . . . . . . . . . . . . . . . . . . . 11
5.2 Display software WinEEG . . . . . . . . . . . . . . . . . . . . . . . . 11
5.3 Electro-Cap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.4 The initial view Matlab . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.5 Example of Display Raw Data Subject . . . . . . . . . . . . . . . . . 13
5.6 Example of Display Raw Data Subject . . . . . . . . . . . . . . . . . 13
5.7 Display EEG data after Bandpass Filter . . . . . . . . . . . . . . . . 13
5.8 The Brain Sigbal Rilex Close Pre . . . . . . . . . . . . . . . . . . . . 14
5.9 The brain signals Rilex Close Post after 4 minutes . . . . . . . . . . . 15
5.10 Rilex Close Post after 65 minutes . . . . . . . . . . . . . . . . . . . . 15
iii
Chapter 1
Introduction
1.1 Background
Drug broadcasting among youth and teenagers cannot be denied and still consume it
in the environment around us. It seems to be for health and a little future [17]
The danger of drugs for addicts and adolescents, students is very much and if not
immediately spent on buying drugs then drug use will overdose and one of the factors
that can be felt from drug use can damage the mental and cure the tragic. [18]
The e ect parameters derived from quantitative EEG analysis seem to be very
suitable for marking relationships between nakoba users or not drug users. EEG pa-
rameters represent many characteristics of pharmacodynamic actions that are ideal,
sustainable, and objective These features provide an opportunity to nd out the
relationship between the e ects for drugs on individuals, which makes valuable infor-
mation about brain waves of drug users.[10]
Electroencephalogram (EEG) is an activity to record the electrical activity of
brain neurons. EEG is being used.[5] Using EEG, the condition of relaxing or not
drug users can be observed. EEG signals are widely used to detect brain disorders
in the world of health. However, EEG signals are not made to be ready to detect
brain abnormalities automatically. Therefore, the characteristics of the EEG signal
are safe.[19] Simultaneous EEG modulation of the volume of BOLD activity from the
target brain region and investigation of electrophysiological activities related to the re-
sults of objects using illegal drugs will show human brain waves.[3] electroencephalog-
raphy (EEG), we investigated and compared reciprocal accuracy and e ciency and
direct EEG forward approaches for brain electrical current sources based on existing
methods, adjoint (AA) approaches and partial integration approaches in relation to
the concept of data transfer matrices. By analyzing numerical results, comparing
with EEG's advanced potential is derived analytically and estimates complexity [14],
and to overcome the e ects of linear sources attached to standard interaction steps,
alternative phases and connectivity steps based on amplitude correlations, such as
imaginary coherence and orthogonal amplitude correlations proposed. De ned to be
insensitive to zero-lag correlation, this technique has become increasingly popular in
the identi cation of correlations that cannot be attributed to eld spread or volume
conduction. This fundamental problem is region-based analysis and importance-for-
all mapping. Most importantly, beyond de ning and describing the problem of false
interactions, we provide strict quanti cation of these e ects through extensive simu-
lations. In addition, we further demonstrated that the mixing signal also limits the
separation of the neuron phase and amplitude correlation
Methadone is a type of synthetic opioid drug, used as an analgesic and for treating
addiction from users of the opioid group, such as heroin, morphine, and codeine. But
1
its improper use can also have a negative impact on health. Although methadone
is indispensable for medical treatment and services, especially drug addicts, if it
is misused or used is not by the standard of treatment, especially if accompanied
by illicit circulation, it will have a very detrimental e ect on individuals or society
,especially the younger generation[7]. Detection of the use of methadone and other
types of drugs is quite a lot done in government agencies, hospitals, and industries to
screen employees/workers who will carry out medical tests. Already many methods
used are detection using urine, using saliva or doing screening by doing brain signal
recording[6]
1.2 Problems Identi cation
Identify the problem based on the background are:
1. How the human brain wave monitor for drug users
1.3 Objective and Contribution
1.3.1 Objective
1. Monitoring the human brain waves against drug users
1.3.2 Contributione
1. Help monitor brain waves to drug users and the detection of EEG signals
1.4 Scoop and Environtment
The scope of the study conducted by researchers are:
1. Showing graph EEG
2. Real-Time Monitoring brain waves and determine the characteristics based on
the EEG signals.
Chapter 2
Related Works
2.1 Electroencephalogram
Elektroensefalogra (EEG) is a test that is widely used to detect brain waves. This
signal is recorded by the deviceElectroencephalography, Is hardware that functions
record the electrical activity of a brainwave. The working principle of this EEG detects
activity electricity from the brains of people with that recorded by silver electrodes
that are installed by trained technicians in the scalp. In this study, participants
brain activity was observed using an electroencephalogram (EEG) while each of the
participants completed spatial ability assessments[16]
The medical community using EEG, among others for the diagnosis of diseases
associated with the brain and psychiatric disorders. EEG is also applied to detect
patterns of mind or mental condition of a person. Visual observation of the EEG
signal directly is challenging given the EEG signal amplitude is low and thus a very
intricate pattern. Besides, the EEG signals are strongly in
uenced by a variety of
variables, such as mental state, health, the activity of the patient, the recording
environment, electrical interference from other organs such external stimuli, and the
age of the patient. The nature of EEG signals is generally non-stationary and random,
so that adds to the complexity in the processing of EEG signals. However,[15]
2.2 Brain
The brain is a central nervous system consisting of billions of cells called neurons.
Each neuron communicates with each other and emits electric waves or commonly
known as brain waves. Brain waves can be measured using an electroencephalogram
(EEG). Brain waves produce frequencies that vary between 0.5-30 Hz and are classi ed
into delta, theta, alpha, and beta waves. Each stream has di erent characteristics
and shows a person's mental state. where every brain activity is very dependent on
the situation of drug users, and where loyal users must be the brain waves produced
vary between brain waves produced by the drug user, and where the state of the brain
mostly produces internal rhythms from outside the user's drug awareness.[12]
2.2.1 Brain Waves
Brain waves can be measured with equipment Electroencephalograph (EEG). It is
known that the frequency of brain waves generated by neurons varies between 0-
30 Hz and is classi ed into a delta wave, theta, alpha, and beta. Each stream has
3
di erent characteristics and indicate a person's mental condition. Human brain waves
have di erent frequency and amplitude range di erent[8], so it is divided into several
types of waves as follows:
1. Delta waves
Delta waves have frequency waves that are worth 1.5 <4 Hz. Delta waves are
one of the slowest waves of other brain waves, in this condition a person's body
carries out a process of self-healing, repairing damage to body tissues, and
actively producing new cells when a person is sound asleep[1]
2. Theta waves
Theta waveform has a frequency value between 4 -8 Hz with a voltage amplitude
reaches 10 V. These waves are generated when a person is experiencing light
sleep or drowsiness[4]
3. Alpha waves
Alpha waves have a frequency value between 8-13 Hz, where this frequency
is a control of the body or conscious and subconscious mind where a person
can remember a dream or more signi cantly someone does meditation (mild
meditation)[13]
4. Beta waves
Types Beta waves have a frequency of between 14-19 Hz and 20-30 Hz in beta
waves indicating that a person is experiencing mental wakefulness or someone
can control himself or think that he is tired because a lot of work presses or
complicated problem solving[9]
5. Gamma waves
Gamma waveforms have a frequency value of 30-40 Hz. These waves are pro-
duced when someone experiences extraordinary mental activity or can be said to
be bad usually someone who is undergoing a match, is panicking or is scared[11]
Figure 2.1: Form biolistic Brain Signals
4
2.3 MATLAB
MATLAB is a high-level programming language for applications in various elds,
such as numeric computation, analysis, and data visualization software and algorithm
development, and design of the model system. (MathWorks, 2017) MATLAB toolbox
is complemented by a wide range to support speci c tasks, either developed directly
by MathWorks and third parties. In this study, the toolkit is used eld trip.
A eld trip is an open source software for EEG analysis, magnetoencephalography
(MEG), electrophysiological data and more. The software supports a variety of func-
tions for the study of EEG as a pre-processing of data, ERP analysis, classi cation,
and others. Here is a preview of the software is MATLAB icon 2017 in Figure 2.2
2.4 Feature Extraction
Is a feature extraction stage signal processing into a vector that contains the relevant
values, so that the message can be analyzed and taken the information for further
processing? In this phase, the raw signal data will be ltered by a bandpass lter,
so that the signal will be passed the signal value of the frequency based on the
sampling frequency is 5-30 Hz. Signal lter results are still in the time domain so
that the information can not be retrieved. Furthermore, the signal is converted to the
frequency domain peril by using the P-300. These values are ideally able to provide
the information contained in the EEG signals associated mental states to be identi ed
and reject artifacts and other bene ts that are not relevant (Lotte, 2014). Examples
of the signal features include statistical feature frequency (Abootalebi et al.,(Subasi,
2014; Panel al, 2014, Ramirez-Cortes et al., 2010).
In the process of classi cation features, the features have been extracted signals
are categorized by class or label set. These classes are related to mental states to
be identi ed (Fraser, 2014). EEG signal processing is generally done automatically,
not least in the process of classi cation features and optimizes the system. Menuurut
Lotte (2014), there are two processes in the design of the EEG signal classi cation
system based machines, namely:
1. raining is the stage of system optimization through parameter tuning features
and training system.
2. Testing a testing stage classi cation system has been trained * with using data
that is not previously known.
Chapter 3
Case Study
3.1 History of case study
Informatics Research Center (IRC) was formed on May 9, 2018. The IRC Formation
Polytechnic Lecturer and Pos Indonesia conducting formatting and concept formation
in the Laboratory IRC IRC to carry out a series of studies. IRC is a research program
initiated by the faculty of Informatics Polytechnic Pos Indonesia. The program is
much related to technology and research.
By using technology that is already stable, the research process can run as well
as solving the problems that exist around. Some issues related to the research has
been conceptualized from professors and formation IRC to realize the VISION and
MISSION IRC.
Figure 3.1: The company logo
3.2 Vision and Mission of case study
3.2.1 Company vision
Being a national collaborative center Tri Dharma universities throughout Indonesia
in 2020
3.2.2 Company mission
1. Increased publicity work of students and faculty in a reputed journal publica-
tions International
2. Utilization of research results to the community technology optimally.
6
3. Accelerating the development of open source applications
4. Work crowdsourcing system
5. Always updated with news and information about technology
3.3 Organization Structure of case study
3.3.1 Organization Structure of case study
Figure 3.2: IRC Organizational Structure
3.3.2 Job Description of case study
Job description that of the IRC are as follows:
1. EEG data processing
2. Research Electroencephalogram (EEG)
3.4 Data description source
3.4.1 Description Participants Internship Job Type I
In this internship activities, Internship program authors conducted the rst in IRC
and positions as an internship as a researcher where the function of the researcher is
researching IRC.
Existing jobs at the researcher, among others conduct research on Electroen-
cephalogram on brain waves.
7
3.4.2 Scope Internship I
In this internship, program writers are in a division researcher to examine EEG. In
EEG study, the authors conducted a development section is standard or abnormal
signal detection on the EEG signal.
3.4.3 Responsibilities of Participants Internship I
The responsibility of the author during this internship program, among others, can
undertake the task given by an external supervisor, take the development of problems
that occur in the EEG.
3.4.4 Participants Job Description Internship How Far I
In this internship, program author gets some work related to the development of the
EEG. Work is researching drug users by users with EEG signal detection.
Chapter 4
Methods
4.1 Research Methodology
Figure 4.1: Flowchart research
4.2 Method Description
Stages (groove strategies) the researchers used in the analysis of EEG data is as
follows:
1. Determine the object of research.
Determine what object will be investigated in the research activity. Some things
need to be understood to determine and construct an object of research in a
research method, which relates to the purpose of research, besides that, each
object of research and what criteria can be used as the purpose of the research
that we do[19], in this study I had the opportunity to examine a drug user in
the brain of the drug user and in this study I assessed brain waves by using
EEG (Electrocardiograph) determination of brain waves to be explained here
based on the amplitude of certain objects
9
2. Identi cation of problems
Problem identi cation is the process and result of problem recognition. In other
words, problem identi cation is one of the research processes which is arguably
the most important among other procedures. Research problems will determine
the quality of research, in this step the researcher can nd out more, can by
conducting observations, reading the literature identi es one aspect that has
been determined with the relevant environment (drug users) that is relevant.[2]
3. Research purposes
The purpose of this study was to obtain a formula for the results of research
through the process of searching, discovering, developing, and testing surveys
conducted.In this phase the researchers aimed to process the data. The purpose
of this study could provide a basis for further developing research that builds
evidence about brain waves of drug users[2]
4. Data collection
Data collection was conducted to obtain information required in achieving the
goal of research being done. Before attending the study, the researchers usually
have had a notion based on the theory that he used, each study had a data
collection process that is di erent depending on the type of research that is
being researched
5. Data processing
The image below is the process of processing data using Wavelet. the processed
data is the data obtained from the EEG signal recording process, Bandpass
Filter and ICA data after processing from Matlab. At this stage of data pro-
cessing researchers conduct data processing on someone who is a drug user, at
this stage researchers will also get the results of data processing in the form of
amplitude..
Figure 4.2: Data Processing
6. results
The results of processing the data researchers obtain information that has been
processed beforehand and researchers get the results of brain waves of drug
users.
Chapter 5
Experiment and Result
5.1 Experiment
5.1.1 Tools Used
To process the EEG signal, there are a wide variety of devices that can be used in
EEG data processing, among others:
5.1.1.1 EEG ampli er Mitsar -202 and WinEEG
For the EEG signal acquisition phase in this study, the hardware used is an EEG
ampli er Mitsar-202 32 channels. Display devices of this ampli er can be observed
in Figure 5.1
Figure 5.1: EEG ampli er system Mitsar-202
To record EEG signals using this ampli er, the ampli er device is connected to
a PC using USB. Furthermore, speakers WinEEG operated via software on the PC.
WinEEG program display can be observed in Figure 5.2.
Figure 5.2: Display software WinEEG
11
5.1.1.2 Electro-Cap
Electro-Cap a hat-shaped device that serves to facilitate the placement of EEG elec-
trodes. This device consists of some lead electrodes connected to an ampli er via
adapters (Electro-Cap International, Inc., 2015). Views Electro-Cap can be observed
in Figure 5.3
Figure 5.3: Electro-Cap
5.1.1.3 MATLAB
MATLAB is a high-level programming language for applications in various elds,
such as numeric computation, analysis, and data visualization software and algorithm
development, and design of the model system. (MathWorks, 2017) MATLAB toolbox
is complemented by a wide range to support speci c tasks, either developed directly
by MathWorks and third parties. In this study, the toolkit is used eld trip.
The eld trip is an open source software for EEG analysis, magnetoencephalogra-
phy (MEG), electrophysiological data and more. The software supports a variety of
functions for the study of EEG as a pre-processing of data, ERP analysis, classi ca-
tion, and others. Here is a preview of the software is MATLAB icon 2017.
Figure 5.4: The initial view Matlab
5.2 Data Processing
5.2.1 Pre-Processing
Pre-processing is beginning the process of signal processing that consists of raw, and
bandpass lter and Brain Mapping. Such procedures would improve the signal by
removing artifacts, and present signals are more easily analyzed.
12
5.2.1.1 raw Data
To record EEG signals using this ampli er, the ampli er device is connected to a
PC using USB. Furthermore, speakers WinEEG operated via software on the PC.
WinEEG program display can be observed in Figure 5.5
Figure 5.5: Example of Display Raw Data Subject
Raw Data is the raw data that has not been treated at all. Wave signal is still
very rough and irregular caused by artifacts in the message. Raw EEG signal data
on each channel can be seen
Figure 5.6: Example of Display Raw Data Subject
5.2.1.2 bandpass Filter
Bandpass lter with a frequency range of 3 Hz to 30 Hz is used to the raw data.
Bandpass lters on the EEG signals are shown in Figure 5.6
Figure 5.7: Display EEG data after Bandpass Filter
13
5.2.2 Data Processing Using Wavelet
The wavelet transform is a method of signal processing requires the workings resemble
signal analysis using the Fourier transform by splitting the signal to be analyzed into
several parts. The di erence, tell the Fourier transform of a signal frequency infor-
mation, but not with the timing information but the wavelet transform signal in the
time domain into signals in the time and frequency, in this case, is formed into an area
of translation and scale. Translation (reading) is a form of transformation from the
time domain translation associated with the location of the window function, where
window be moved along the incoming signal. Scale (scale) is a form of transformation
of frequency, where the value scales inversely proportional to the frequency value.
5.3 Result
At this stage, to see the most dominant brain activity. Here is a scene Wavelet
application con guration and the results of Brain Mapping one subject
1. Result Rilex Close Pre
Figure 5.8: The Brain Sigbal Rilex Close Pre
picture Result Rilex Close Pre above is the result of processing data using
wavelets. the graph above is the result of recording subject 1 data in relaxed
and closed eyes. in this condition, the subject is told to close his eyes and
not think of anything. after recording and processing data, the subject's brain
condition can be seen based on the picture above. the result, the central subject
brain condition is 160Hz, Frontal 40Hz and the ociptal Pariental is at 180Hz
2. Result Rilex Close Post after 4 minutes
picture Result Rilex Close Post after 4 minutes above is the result of processing
data using wavelets. The graph above is the result of recording subject 1 data
after the administration of methadone for 4 minutes. in this condition, data
in colleagues used WinEEG on subjects who had been given methadone. after
recording and processing data, the subject's brain condition can be seen based
on the picture above. As a result, the central subject brain condition is 150Hz,
Frontal 190Hz, Ociptal Pariental is at 160Hz and temporally 40Hz
14
Figure 5.9: The brain signals Rilex Close Post after 4 minutes
Figure 5.10: Rilex Close Post after 65 minutes
3. Result Rilex close post after 65 minutes
picture Result Rilex close post after 65 minutes above is the result of processing
data using wavelets. the graph above is the result of recording subject 1 data
after giving methadone for 65 minutes. in this condition, data in colleagues
used WinEEG on subjects who had been given methadone. after recording and
processing data, the subject's brain condition can be seen based on the picture
above. As a result, the central subject brain condition is 160Hz, Frontal 200Hz,
and the ociptal Pariental is at 110Hz.
Chapter 6
Conclusion
6.1 Conclussion of Problems
By paying attention to data processing and analysis in the previous chapter it can
be concluded that wavelet is a signal processing method that requires work methods
similar to Fourier signal decomposition can be used to identify how a person's brain
is a drug user or not a drug user.
6.2 Conclusion of Method
The conclusions obtained in this research method are where the researcher determines
the method used to process the data from the object determined by the research will
determine the quality of the research. In this step the researcher can nd out more, by
observing, reading the literature identifying one predetermined aspect with relevant
environmental (drug users) relevan.
6.3 Conclusion of Experiment
In this study, eeg signals that have been recorded using WINEEG and analysis using
WAVELET can be concluded that the condition of the otas of the person being studied
can be observed. Observations made by comparing the results of the subject's brain
data record before and after being given a stimulus. This analysis also shows large
variations due to drugs or methadone. Therefore, the use of the proposed method, the
assessment of the brain condition of drug users using WAVELET, the brain condition
of drug users and the identi cation of e cient EEG signals can help in research.
16
Chapter 7
Discussion
Methadone is a type of synthetic opioid drug, used as an analgesic and for treating
addiction from users of the opioid group, such as heroin, morphine, and codeine. But
improper use can also have a negative impact on health. Although methadone is
very necessary for medical care and services, especially drug addicts, if it is misused
or used is not in accordance with the standard of care, especially if accompanied
by illicit circulation, it will have a very detrimental e ect on individuals or society,
Electroencephalogram (EEG) is an activity to record the electrical activity of brain
neurons. Using EEG, the condition of relaxing or not drug users can be observed.
EEG signals are widely used to detect brain disorders in the world of health. However,
EEG signals are not made to be ready to detect brain abnormalities automatically.
Therefore, the characteristics of the EEG signal are safe.Simultaneous EEG modula-
tion of the volume of BOLD activity from the target brain region and investigation
of electrophysiological activity related to the results of objects using illegal drugs will
show human brain waves.
The conclusions obtained in this research method are where the researcher de-
terminesthe method used to process the data from the object determined by the
research willdetermine the quality of the research. In this step the researcher can nd
out more, byobserving, reading the literature identifying one predetermined aspect
with relevantenvironmental (drug users) relevan.
In this study, eeg signals that have been recorded using WINEEG and analysis
usingWAVELET can be concluded that the condition of the otas of the person being
studiedcan be observed. Observations made by comparing the results of the sub-
jects braindata record before and after being given a stimulus. This analysis also
shows largevariations due to drugs or methadone. Therefore, the use of the proposed
method, theassessment of the brain condition of drug users using WAVELET, the
brain conditionof drug users and the identi cation of e cient EEG signals can help
in research
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