MINISTRY OF EDUCATION AND TRAINING MINISTRY OF TRANSPORT
HO CHI MINH CITY UNIVERSITY OF TRANSPORT
LÂM QUANG CHUYÊN
NEURAL NETWORK IN THE WHEELCHAIR
CONTROL SYSTEM FOR SEVERE DISABILIED
PEOPLE USING EEG SIGNAL AND CAMERA
FIELD OF STUDY
TECHNIQUE OF CONTROL AND AUTOMATION
CODE: 9520216
SUPERVISORS:
1. Assoc. Prof., Dr. NGUYỄN HỮU KHƯƠNG
2. Assoc. Prof., Dr. VÕ CÔNG PHƯƠNG
HO CHI MINH CITY– 03/2020
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ABSTRACT
Nowadays, EEG signal, one of the most important field was
interested by science researchers, the main purpose of research is to
support application devlepments, diagnose and find out pathological
of human as stress, depression, epileptic, alzheimer, brain trauma…,
however, in the field of automatic control serving for human,
especially for disabilities people, has not been studied so much.
For long time ago, recording and processing the EEGs or ECGs
signal was the work of neurologists or cardiologists, nowadays, with
the development of modern signal processing and analysis tools such
as neural networks and AI systems, such signals can be processed to
meet the other needs, such as the control system support human
acitivites.The goal of this thesis is to build a control system, which
support some basic human activities through EEG signal. For
example, wheelchair equipement control for disabled people, meet
today’s pressing social needs.
In this thesis, author researched and analyzed three EEG signal
pre-processing methods as Fourier transform, Wavelet transform and
HHT transform, converting EEG signal to 5 basic EEG waves (Delta,
Theta, Alpha, Beta, Gamma), and then using data clustering technical
before put them into input layer of multilayer neural network. The
neural network was test from singlelayer to multilayer (3 layer).
Author combined the EEG signal processing system with HHT
pre-processing and image processing using multi neural network to
control the wheelchair model with accurate rate 92.4% for group 20
students, this shows the successful in the practical of the thesis.
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THE THESIS STRUCTURE
Chapter 1: Overview – Presenting of the research situation of
EEG signal in domestic and internation, presenting of results have
been researched and published, anayzing unsolved and limited
problems that thesis needs to solve, in addition, the thesis also
presentate the aimed and scope research, the contributions of thesis in
science and reality life.
Chapter 2: Theoretical basis – Presenting of basic knowledge
relatived to thesis as: Fourier Transform, STFT, Wavelet Transform,
HHT…, clustering data and then classifying EEG signal partterns by
multilayer neural network.
Chapter 3: Model Contruction – Presenting how to construct
multilayer neural network, this process was conducted from
singlelayer (detect 2 signal partterns) to multilayer (detect 5 signal
partterns), in addition, author also discusses between EEG signals and
image processing via camera in combination. Each result has
published on international journals or international conference.
Chapter 4: Constructing software and hardware to control the
wheelchair model – This chapter introduced the functions of sotfware,
the image processing to detect the eye direction in combined with EEG
signals processing to achived the final result.
This chapter also present the experimental results had been
performed by student in Ho Chi Minh City Industry and Trade College
(HITU), comparison the experimental results between 2 processes
(image and EEG signal processing), and then the results has been
combined with 2 processes.
Chapter 5: Conclusions and recommendations – this chapter
present the results have been achived compared to thesis requirements
and offering the solutions to develop EEG field more and more
completely.
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CHAPTER 1
OVERVIEW OF EEG SIGNAL
1.1 Researching situation in domestic
In present, the previous researchs in EEG field mainly focused on
motor activities, eye blink and movement of head… to detecting and
classifying these activities could be performed by amplitude threshold
method. There aren’t topics now related in processing and classifying
the difference EEG signal partterns corresponding to difference image
types observation yet, this couldn’t be resolved by amplitude threshold
method but must be resolved by extracted these signal partterns into
specific features via multilayer neural network.
1.2 International research situation
The research works were published at international paper mainly
focused on diagnosing epilepsy sleep disorders, coma and brain death,
stress, depresssion pathological… in automation field as spelling, eye
blink, head movement, mental arithmetic… this were performed by
offline, no mainly focus on resolving in realtime and in control
automation field.
1.3 The content of thesis
At first, author constructed the multilayer neural network base on
raw EEG data from San Diego University (UCSD), to identify and
classify 5 EEG signal partterns corresponding to 5 difference image
types (human, city, landscape, flower and animal), after determining
the feasibility of the multilayer neural network, author conducted on
realtime EEG data combined with camera to increase efficiency, this
has been performed by student of HITU, in the implementation
process, the thesis extracted the feature signal by Hibert Huang
Transform (HHT), clustering data and then using multilayer neural
network, making system work more efficienty and avoiding
“overfitting” problem.
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1.4 The purpose of the research
Finding out the way collect 5 EEG signal partterns easily and
effectively.
Using math tools to transform the EEG signal partterns into
specified extract features, using K-Mean technique to cluster EEG
signal data before putting in multilayer neural network to classify
these partterns.
Combining EEG signals and camera processing aim to identified
and classified process more easily and effectively.
1.5 Object and scope of the research
The main object in thesis is to use multilayer neural network to
classify 5 EEG signal partterns into control commands corresponding
to 5 commands to control the wheelchair as: Forward, Turn right, Turn
left, Reverse and Stop. In thesis also mention to image processing to
detect the eye direction aim to help the system work more easily and
effectivly. However, the thesis don’t focus more on image processing,
but on EEG signal processing.
1.6 The contribution of thesis
1.6.1 The contribution about theory
Find out the observation board which easy to use to collect data,
combine scientifically between feature extraction algorithm and
cluster data before putting into neural network to classify data
partterns.
1.6.2 Practical contribution
The experimental result of thesis show that the EEG signal
partterns classification through eye observation (with differnce image
partterns), for people who has mind and eyes as normal people could
absolutely performance.
CHAPTER 2 - THEORETICAL BASIS
2.1 EEG signal and its characterizations
Delta wave (0 – 3 Hz), the highest amplitude as figure 2.1, it often
appear at the child up to 1 year old and adult when sleeping, well sleep.
It represents the grey matter of the brain. This wave usually appears
everywhere on the scalp.