PILOT STUDY OF USING EEG AS A BIOMARKER TO DIAGNOSE AUTISM
A Thesis
Presented to
The Faculty of the College of Graduate Studies
Lamar University
In Partial Fulfillment
of the Requirements for the Degree
Master of Science in Electrical Engineering
by
Anita E. Igberaese
May 2016
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PILOT STUDY OF USING EEG AS A BIOMARKER TO DIAGNOSE AUTISM
ANITA E. IGBERAESE
Approved:
______________________________
Gleb V. Tcheslavski
Supervising Professor
______________________________
Hassan Zargarzadeh
Committee member
______________________________
Donna Sheperis
Committee member
______________________________
Harley Myler
Chair, Department of Electrical Engineering
______________________________
Srinivas Palanki,
Dean, College of Engineering
______________________________
William E. Harn
Dean, College of Graduate Studies
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©2016 by Anita E. Igberaese
All rights reserved. No part of this work can be reproduced in any form without prior
permission except as indicated by the “Fair Use” clause of the copyright law. Passages,
images, or ideas taken from this work must be properly credited in any written or
published materials.
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ABSTRACT
PILOT STUDY OF USING EEG AS A BIOMARKER TO DIAGNOSE AUTISM
by
Anita E. Igberaese
In the 1900s, Autism was referred to by a number of neurological conditions;
however, the word “Autism” was first used by a Swiss psychiatrist, Eugen Bleuler, to
refer to a group of schizophrenia symptoms.
Over a hundred years later, Autism is still grossly misunderstood. Autism is one
of the diseases in the Autism Spectrum Disorder (ASD). Other diseases include the
Asperger’s syndrome and the Pervasive Developmental Disorder Not Otherwise
Specified (PDD-NOS). They are collectively called spectrum disorders because
diagnoses of any of these disorders have been found to be of varying degree, similar to a
spectrum; only showing a certain amount of symptoms.
This thesis is aimed at proving that Electroencephalography (EEG) can be a
viable biomarker to diagnose Autism. EEG was recorded from 6 subjects; 3 autistic and 3
controls. While recording EEG, the subjects were exposed to a slide show of fairly
familiar images and were asked later to recall and name these images.
EEG was preprocessed to remove noise and artifacts that may contaminate the
EEG Signals. After the process, Power Spectral Density estimates were extracted. The
features – the spectral estimates – were obtained using the Modified Covariance
autoregressive method. The features were tested for similar statistical distribution using
the Kruskal-Wallis method. The latter showed that EEG from the autistic and control
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subjects may originate from statistically different distributions. This observation suggests
that it might be possible to discriminate between EEG features originating from
individuals constituting to two different groups.
Lastly, the extracted features were classified using the K-Nearest neighbor
classification algorithm, with the accuracy up to 89.29%.
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ACKNOWLEDGEMENTS
Although, only my name appears on the front page of this thesis, I owe my
gratitude to everyone who have made this thesis possible, and because of whom my
graduate experience has been one that I will cherish forever.
I would first like to thank my thesis advisor Dr. Gleb Tcheslavski of the Electrical
Engineering Department at Lamar University, for his support, guidance and patience
throughout my research. The door to Dr. Tcheslavski’s office was always open whenever
I ran into trouble spots or had questions about my research or writing. He consistently
encouraged me, and steered me in the right direction whenever he thought I needed it.
I am thankful to my committee members Dr. Donna Sheperis and Dr. Hassan
Zargarzadeh for being so willing to oversee this project; for their generous support and
insightful suggestions on numerous occasions throughout my research. I express my
sincere appreciation to them for always taking out precious time from their busy
schedules to help me progress in my research. I am very grateful to Niki Contreras for the
dedication with which she edited my thesis. She was always a phone call away, and
always ready to make necessary clarifications.
I would like to thank Dr. H.R. Myler, Professor and Chair, Department of
Electrical Engineering, for his constant encouragement, support and help throughout my
program. I would also like to thank Ms. Jane Stanley Capps, the former administrative
associate senior, for being a “mom’ to me; for the warm welcome she gave me by taking
me into her home when I got to Beaumont, Texas; taking me for my doctor’s
appointment and for being such a huge support throughout my pregnancy, till I had my
baby.
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I would like to thank my Uncle and his wife, Mr. and Mrs. Sylvester Anyanti for
all of the support they provided throughout my study at Lamar University. Words cannot
begin to express how grateful I am for all they have invested in my life. They have been
such a great support. Thank you.
Lastly, I would like to thank my family: my siblings, for always urging me on; my
father for always praying for me; my in-laws for all the love they have shown; and my
husband for constantly pushing me to greatness.
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Dedication
I would like to dedicate this thesis to my mum, for believing in me enough to
push me to my best; to my dad for always praying for me; to my son, David, for being the
only motivation to keep studying on some long days; and most importantly, to my
husband for loving me and encouraging me, always reminding me of the best I can be.
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Table of Contents
List of Tables ................................................................................................................... viii
List of Figures .................................................................................................................... ix
1. Introduction ..................................................................................................................... 1
1.1 Introduction ............................................................................................................... 1
2. Background ..................................................................................................................... 6
2.1 EEG History .............................................................................................................. 6
2.2 EEG Production and the Human Brain ..................................................................... 7
2.3 EEG Data Recording ............................................................................................... 11
2.4 EEG Rhythms and Brain Activities ........................................................................ 15
2.5 Noise, Artifacts and Artifacts Reduction ................................................................ 18
3. Experiment Design........................................................................................................ 20
3.1 Background ............................................................................................................. 20
3.2 Experiment Setup .................................................................................................... 21
3.3 Experiment Procedure ............................................................................................. 22
3.4 Data Acquisition ...................................................................................................... 28
4. Signal Analysis ............................................................................................................. 29
4.1 Introduction ............................................................................................................. 29
4.2 EEG Analysis Tools ................................................................................................ 29
4.3 EEG Preprocessing .................................................................................................. 29
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4.3.1 Common Average Reference............................................................................ 30
4.3.2 DC Offset Removal .......................................................................................... 30
4.3.3 Notch Filtering .................................................................................................. 31
4.3.4 Half Second Segmentation and DC Offset Removal from Half Second
Fragments .................................................................................................................. 32
4.4 EEG Feature Extraction .......................................................................................... 33
4.4.1 Power Spectral Density .................................................................................... 33
4.4.2 Power Spectral Density Estimates for Each Half Second Segment ................. 35
4.5 Kruskal-Wallis Test................................................................................................. 36
4.6 KNN Classification ................................................................................................. 37
5. Result and Discussion ................................................................................................... 39
5.1 Power Spectral Density ........................................................................................... 39
5.2 Kruskal-Wallis Test................................................................................................. 48
5.3 K-NN Classification ................................................................................................ 49
6. Conclusion and Future Work ........................................................................................ 51
6.1 Conclusions ............................................................................................................. 51
6.2. Future Work ........................................................................................................... 52
References ......................................................................................................................... 54
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List of Tables
Table
Page
Table 1. Table Showing the Various EEG Waves and Their Properties .......................... 15
Table 2. P-values from Kruskal-Wallis Test..................................................................... 50
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List of Figures
Figures
Page
Figure 1. Neuron (BBC 2015) ............................................................................................ 2
Figure 2. Synapse (NotoriousLTP 2007) ............................................................................ 2
Figure 3. Parts of a Neuron and their function (Human Anatomy 2016) ........................... 8
Figure 4. Synaptic transmission (Wikipedia 2016a) ........................................................... 9
Figure 5. The Forebrain, the Midbrain, and the Hindbrain (NINDS 2015a) .................... 11
Figure 6. Equipment used for EEG recording: Electrode cap, conductive gel, injection,
amplifier unit (Teplan 2002) ............................................................................................. 13
Figure 7 Electrode location of International 10-20 system for EEG recording (Wikipedia
2016b) ............................................................................................................................... 14
Figure 8. 14 Channel electrode placements, including the two reference electrodes CMS
and DRL (Gmac 2009)...................................................................................................... 22
Figure 9. Ellen H. Swallow Richards House Boston MA (Wikipedia 2009) ................... 23
Figure 10. A Bright Orange Gazania flower in Full bloom (Wikipedia 2004)................. 24
Figure 11. Brown Kenneth Bear Plush Toy ...................................................................... 24
Figure 12. President Barack Obama ................................................................................. 25
Figure 13. Volkswagen Passat .......................................................................................... 25
Figure 14. An oak tree ...................................................................................................... 26
Figure 15. An orange ........................................................................................................ 26
Figure 16. A mathematical equation ................................................................................. 27
Figure 17. A cute baby ...................................................................................................... 27
Figure 18. Arizona sunset ................................................................................................. 28
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Figure 19. PSD plot of one electrode ................................................................................ 39
Figure 20. Averaged PSD estimates for AF3 EEG electrode and for ASD and control
participants ........................................................................................................................ 40
Figure 21. Averaged PSD estimates for AF4 EEG electrode and for ASD and control
participants ........................................................................................................................ 41
Figure 22. Averaged PSD estimates for F3 EEG electrode and for ASD and control
participants ........................................................................................................................ 41
Figure 23. Averaged PSD estimates for F4 EEG electrode and for ASD and control
participants ........................................................................................................................ 42
Figure 24. Averaged PSD estimates for F7 EEG electrode and for ASD and control
participants ........................................................................................................................ 42
Figure 25. Averaged PSD estimates for F8 EEG electrode and for ASD and control
participants ........................................................................................................................ 43
Figure 26. Averaged PSD estimates for FC5 EEG electrode and for ASD and control
participants ........................................................................................................................ 43
Figure 27. Averaged PSD estimates for FC6 EEG electrode and for ASD and control
participants ........................................................................................................................ 44
Figure 28. Averaged PSD estimates for O1 EEG electrode and for ASD and control
participants ........................................................................................................................ 44
Figure 29. Averaged PSD estimates for O2 EEG electrode and for ASD and control
participants ........................................................................................................................ 45
Figure 30. Averaged PSD estimates for P7 EEG electrode and for ASD and control
participants ........................................................................................................................ 45
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Figure 31. Averaged PSD estimates for P8 EEG electrode and for ASD and control
participants ........................................................................................................................ 46
Figure 32. Averaged PSD estimates for T7 EEG electrode and for ASD and control
participants ........................................................................................................................ 46
Figure 33. Averaged PSD estimates for T8 EEG electrode and for ASD and control
participants ........................................................................................................................ 47
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Igberaese 1
Chapter 1
Introduction
1.1 Introduction
Electroencephalography is used to measure the electrical activities in the brain.
Richard Caton was the first scientist to discover electric signals in the brain (Caton 1875).
However, a German psychiatrist Hans Berger was the first person to apply this technique
to a Human brain, producing the first human EEG recording in 1924 (Millet 2002).
The name, Electroencephalogram was coined from the Greek words “electro”
(related to the movement of electrons), ‘encephalo’ (the brain) and ‘gram’ (a picture, a
drawing, a character, etc.) (Online Etymology Dictionary 2015). Put together, it connotes
that the EEG machines are used to “write-out” the electrical activities in the brain. This is
achieved by attaching flat metal discs (electrodes) to the scalp (MedlinePlus 2016). The
electrodes pick up the electrical signals from the brain; the brain communicates by
sending electrical impulses between cells. The EEG is one of the diagnostic tools for
epilepsy and other brain disorder (Mayo Clinic 2016).
The brain, which is one of the largest and most complex organs in the human
body, is made up of billions of nerves (NINDS 2015a). These nerves have billion of
connections that carry “messages” around the body, controlling nearly every other part of
the body. The brain consists of Frontal lobe, Parietal lobe, Temporal lobe and Occipital
lobe (Goetz 2007). Each part is responsible for specific functions, including
communication, thinking, movement, cognitive abilities, language, metabolism etc.
Messages are passed from the brain to various parts of the body with the help of cells
called neurons. Figure 1 below shows a graphical representation of a neuron. A neuron is
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Igberaese 2
divided into 3 parts: the Cell Body, the Dendrites, and the Axon (NINDS 2015b). The
Cell Body contains the nucleus, which determines function and type of each neuron. The
Dendrites are responsible for receiving messages from other nerve cells, and the Axon is
the part of the neutron where signals travel to another neuron or cell.
Figure 1. Neuron (BBC 2015)
Figure 2. Synapse (NotoriousLTP 2007)
When the “message” reaches the end of the axon, it causes the release of tiny sacs
with neurotransmitters that are passed to the synapse. Figure 2, above, illustrates the
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synapse. The synapse performs the function of passing signals from one neuron to
another cell. The neurotransmitters are then attached to receptors of the neighboring cell.
These processes take place very quickly and automatically. However, when a
problem occurs, parts of the body tend to malfunction and the human body functions are
altered. One of such problems is as describe in Autism Spectrum Disorder. ASD is
described as a group of neurological disorders; listed under it are Autistic Disorder,
Asperger’s Syndrome, and Pervasive Development Disorder, not otherwise specified
(PDD-NOS) (WebMD 2015a).
Children diagnosed with ASD usually have similar symptoms, such as speech and
language delay, general developmental delay, and behavioral difficulties. In part, this is
due to the fact that most (although not all) children with ASD have other learning
problems, significant language delay or disorder, or both, and additional symptoms from
related diseases, such as dyspraxia and behavior problems (Charman et al. 2005).
Before ASD was understood, people, who showed symptoms depicting Autism or
similar disorders in the ASD, were deemed mentally retarded. They were mostly
misunderstood and where locked up away from a public view. This easily gave ASD a
negative connotation.
Currently, ASD has been researched considerably; therefore, the disorder’s
information has gained awareness. New studies are forming and publications are
emerging. Autism Spectrum Disorder has been researched by Matson (Matson 2007) and
colleagues (Matson and Wilkins 2007, 28-37) identified a set of neurodevelopmental
disorder that covers the lifespan. Some of the most prevalent symptoms include deficits
in communications (Balconi and Carrera 2007; Lee et al. 2007; Schlosser et al. 2007). In
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Igberaese 4
addition, repetitive behaviors are symptoms seen in individuals with ASD (MacDonald et
al 2007; Matson and Wilkins 2007, 28-37). Another common symptom seen in ASD
sufferers is lack of social skills (Chung et al. 2007; Hilton et al. 2007; Matson 2007).
These features are sometimes compounded by intellectual disabilities (Ben-Itzchak and
Zachor 2007, 89-195; Matson 2007). Research in this field is usually dominated by
psychologists, psychiatrists, and human development and disabilities scientists. The
corresponding tests usually explore the relations between the severity of autism, cognitive
abilities, and social and communication skills in children.
Considerable research was conducted on people with ASD, while using their EEG
and aiming to reveal the differences between the brain of the ASD and control
participants. Daoust and colleagues carried out tests on EEG spectral analysis of
wakefulness and Rapid Eye Movement (REM) sleep in high functioning ASD individuals
(Daoust et al. 2004). Authors suggest that persons with ASD show lower Beta frequency
wave activity during REM sleep over cortical visual areas. Wang and colleagues used
EEG to carry out a resting state study of ASD. They reported a major difference in ASD
and control participants that they referred to as a “U-shaped profile of
electrophysiological power,” showing excessive power in low-frequency and highfrequency bands, abnormal functional connectivity, and enhanced power in the left
hemisphere (Wang et al. 2013).
The present project is aimed at showing the abnormalities in the EEG obtained
from children with Autism. Although children can be diagnosed with autism as young as
6 months, it is rare for autism to be diagnosed before 2 years of age, despite the fact that
in most cases onset is in infancy and is the result of genetic and other organic factors
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Igberaese 5
affecting brain development very early in life. It is hoped that with EEG signals recorded
from children with Autism, infants can be diagnosed as early as possible. This is intended
to be a pilot study; hence conclusions reached can be used to further research in this field.
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Chapter 2
Background
2.1 EEG History
Technologies and findings about EEG can be dated back to the mid-1800s, when
a group of scientists came to an experimental conclusion that there were electrical
properties in living tissues. This was earlier detected in the nerve-muscle of a frog by
Luigi Galvani (Galvani 1791). These discoveries were made before the development of
technologies used to detect electrical signals in the human brain. (Collura 1993, 375). In
1870, Gustav Fritsch and Eduard Hitzig carried out experiments that showed motor
response to stimulations from a galvanometer, on an anesthetized dog. Gustav Fritsch
was motivated to further the study of electrical activities in living tissues after he
observed some muscular movement, while dressing the head injuries on injured soldiers
(Young 1970).
All of these preceded the actual findings of electrical activities in the brain.
Richard Caton was the first to record electrical activity of a functioning brain. He used a
mirror galvanometer, and his test subjects were rabbits and monkeys. He reported that
using William Thomson’s reflecting galvanometer and Du Bois-Reymond’s nonpolarized electrodes eliminated a fair amount of artifact and noise (Caton 1877, 23-24).
He went further in his study and tested 40 cats, rabbits and monkeys, confirming his
findings. Caton recorded various responses to stimuli, such as the sight of food, pinching
their skin, and retinal light stimulation. He was able to isolate parts of the brain
responsible for different kinds of stimuli from investigations carried out by David Ferrier.
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V.Y. Danilevsky also published a thesis, in which he reported recording electrical
activities from the brain of an animal (Collura 1993, 375).
About 50 years later, the first human EEG recoding was produced when Dr. Hans
Berger observed EEG from a 17-year-old college student. He first became interested in
the electrical signals in the brain in 1902, while studying the change in temperature of the
cerebral cortex of a dog in response to stimuli (Berger and Gloor 1970, 562-563). He
finally recorded EEG signals in the brain of a human being, in 1924, while operating on a
17-year old’s brain, which had a tumor, with the surface of the cortex exposed (Collura
1993, 375). He reported 14 recordings between 1924 and 1938 (Berger and Gloor 1970,
562-563) including one from a 40-year old man, who had surgery for gliosarcoma; and
about 73 different EEGs from his son, Klaus (Collura 1993, 375). After Dr. Hans Berger,
there have been several scientists, who have taken interests in the study of EEG, and have
continuously improved the EEG instrumentation.
2.2 EEG Production and the Human Brain
The human brain is responsible for nearly all bodily and mental function of a
human being. The brain and the Spinal Cord make up the Central Nervous System (CNS)
(Col 1996). Nerves connect the CNS to the rest of the body, creating a network all over
the body. The “messages” are transmitted from the brain to every other part of the body
through this network. The brain is made up of neurons and glial cells. Glial cells, also
called Neuroglia, are the supportive and protective cells of the CNS. They surround the
neurons, providing protection between them. They are non-neuronal cells, and do not
conduct electrical impulses (Jessen and Mirsky 1980). The neurons, on the other hand,
conduct electrical impulses. They are considered the core component of the brain. There
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are about 100 billion neurons in the human brain, and the network of neurons is how
messages are passed from the brain to other parts of the body. The neurons pass
information by sending out electro-chemical signals. Interestingly, the neurons are the
only cells in the body that don’t divide up, or die. They are first developed while a fetus
is growing and continue to develop until up to a few months after birth. After then, the
neuron may increase in size until a person is about 18 years. The only part of the brain
where neurogenesis continues throughout the human life is the hippocampus, where
memory is stored (The Human Memory 2015).
Figure 3 below shows the parts of a neuron, and their functions. The neuron is
made up of the cell body or soma, dendrites and an axon. All these are essential in
transmission of messages. The cell body contains the cytoplasm and the nucleus, which
control the cells genetic makeup; hence determining the specific function of the neuron.
Figure 3. Parts of a Neuron and their function (Human Anatomy 2016)
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Each neuron produces a voltage difference across its membrane. This is due to the
ion difference of sodium, potassium, chloride, and calcium in the cell. Each of these
chemical components has a different charge. Electrical activity is seen when the voltage
of a cell changes significantly, creating a nerve impulse. The change in voltage happens
when chemicals, also called Neurotransmitters, are sent out from neuron. When this
happens, brain waves are detected (The Human Memory 2015). These brain waves are
detected as EEG signals. The EEG machine detects the difference in electrical potentials
resulting for neural activities in the brain (Niedermeyer and Lopes da Silva 2005).
When a message is transmitted by a neuron, such transmission occurs through the
axon. The axon looks like a long tail, which varies in length, from a few millimeters to as
long a few feet. The electrical pulse travels along the axon, and is transferred across the
synapse to the next neuron. The dendrites, which look like branches of a tree, receive the
messages being passed from neuron to neuron (NIND 2015). Figure 4, below, shows a
close up of how information is transmitted by the synapse.
Figure 4. Synaptic transmission (Wikipedia 2016a
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Igberaese 10
The brain contains tissues of two types: the gray matter, which accounts for about
40% of the brain, and the white matter occupying about 60% of the brain. These tissues
contain different types of neurons. The gray matter contains mostly unmyelinated
neurons. This means they lack myelin sheath between the neurons. The gray matter
regions are where nerve connections and processing are done. The white matter, on the
other hand, has mostly myelinated neurons, which help connect regions of the gray
matter to each other, and the rest of the CNS. They are necessary because myelinated
neurons transmit signals faster than unmyelinated neurons (Alberts 2008, 627-630).
The brain is divided anatomically into the Forebrain (Cerebrum), the Midbrain,
and the Hindbrain (Cerebellum). The Forebrain is the largest part of the human brain. The
cerebrum is at the topmost part of the brain and it is where intellectual activities take
place. This is where memories are stored, and this is the part of the brain that allows
humans to think and imagine; recognize people and interact. The Midbrain is located at
the topmost part of the brainstem, and it allows for human reflex actions such as blinking
of the eye. The Hindbrain is just at the top of the spinal cord. This is the part of the brain
used to control body functions such as heart rate and breathing. It also helps to coordinate
movement (NINDS 2015a).
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Igberaese 11
Figure 5. The Forebrain, the Midbrain, and the Hindbrain (NINDS 2015a)
The Cerebrum is divided into the Frontal lobe, the Parietal lobe, the Temporal
lobe, and the Occipital lobe. The names of the lobes are derived from the names of the
bones of the skull over them. The Frontal lobe controls behavior, judgement, problem
solving, muscle movement, and physical reaction, among other things. The Parietal Lobe
controls the sense of touch, sensory comprehension, some language and reading function.
The Temporal lobe controls auditory function, some behavior and emotions function, and
sense of identity; while the Occipital lobe controls vision and reading.
2.3 EEG Data Recording
The electrical signals from the brain can be detected by EEG electrodes.
Depending on the specific reason and application of EEG, the electrodes may be placed
non-invasively, as is the case with Electroencephalogram; or invasively, in which case
the electrodes are placed in the epidural and subdural parts of the brain to produce
Electrocorticogram (ECoG). They may also be measured using depth probes; in this
instance, an Electrogram is recorded (Teplan 2002).
In the earliest history of EEG, the technology of the day was used to obtain EEG
signals. These technologies were already in existence and where used for other
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applications ranging from weather equipment and audio engineering to military
surveillance. At that time, there was no specialized EEG equipment. Luigi Galvani and
Alessandro Volta used an electrometer, which used a fine gold leaf to detect electrical
activity; other scientist used equipment like D’Arsonval galvanometer, the Capillary
Electrometer, the Einthnoven String Galvanometer, among other items (Collura 1993,
375).
Presently, EEG is typically detected by small flat discs of metals, of about 10 mm as
electrodes. Figure 6 illustrates a simple EEG acquisition system. The EEG recording
system consists of the following:

Electrodes with conductive media. The electrode may be the disposable type
(usually pre-gelled or gel-less); reusable disc electrodes (gold, silver or tin);
electrode caps; saline-based electrodes; and needle electrodes.

Amplifiers with filters.

A/D converter.

Recording device.
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Figure 6. Equipment used for EEG recording: Electrode cap, conductive gel, injection,
amplifier unit (Teplan 2002)
The electrodes detect electrical signals, which typically range from 10µv to
100µV, from the scalp. The signal is amplified to bring the voltage to a range in which it
can be digitized. The amplified signals are then converted from analog to digital form by
the A/D converter. The data is then stored in the recording device, which is usually a
personal computer (Teplan 2002). Reading the EEG signals on the scalp is possible
because of the potential differences between the recording (active) electrodes and the
reference electrode. The ground electrode is used to obtain the differential voltage, by
subtracting the same voltage values from voltage values at the active electrode and the
reference electrode (Teplan 2002).
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The electrodes are positioned on the subject’s scalp according to the International
10-20 System. This is an internationally recognized and developed method of placing
electrodes on the scalp, for the purpose of EEG experiment. This system was developed
in consideration of the location of the cerebral cortex; making sure the electrodes are
located such that relevant signals are recorded.
Figure 7 Electrode location of International 10-20 system for EEG recording (Wikipedia
2016b)
Figure 7 above shows the electrode placement for a 21-electrode system. The
letters seen in the diagram represent the parts of the brain it records: F is the Frontal lobe,
T is for Temporal lobe, C is for Central lobe, P is for Parietal lobe and O is for Occipital
lobe. The lettering A refer to the earlobes; Pg refers to nasopharyngeal; and Fp refers to
frontal polar site. The numbering depicts the side of the brain being recorded. The odd
numbers refer to the left half of the brain, while the even number refers to the right side
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of the brain. The Nasion and Inion serve as landmarks for placing electrodes; the Nasion
is places above the bridge of the nose, between the eyes, while the inion is places at the
base of the skull, at the back of the head. The spacing between electrodes is usually 10%
- 20% of the total distance between ear to ear or between the front and back of the head.
2.4 EEG Rhythms and Brain Activities
The electrodes are able to detect EEG signals, which are continuous electrical
waveforms of varying frequency and amplitude depending on the part of the brain nearby
the electrodes and the activity being carried out by the subject at that time. These signals
are otherwise called EEG rhythms. EEG rhythms are classified according to frequency
bands. These classifications are as shown in the Table 1 below:
Table 1. Table Showing the Various EEG Waves and Their Properties
EEG
Frequency
Rhythm range
Delta
 4Hz
Basic features
Wave Pattern
This is the slowest
brain wave, usually
generated when a
person is in deep
meditation or deep
Delta Wave (Wikipedia
2016c)
sleep
Theta
4Hz – 7Hz
This brain wave is
mostly seen in
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children. It is seen in
older children and
Theta Wave (Wikipedia
adults when they are
2016c)
drowsy; just as we
drift off to sleep. It is
also said to be
associated with
relaxed and creative
states.
Alpha
7Hz – 14Hz
Tis brain wave was
first names by Hans
Berger. It is the
Alpha Wave (Wikipedia
Posterior Basic
2016c)
Rhythm, usually seen
in the Posterior part
of the head. This
brain wave is
recorded when the
eyes are closed in
relaxation
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Beta
15Hz –
This brain wave is
30Hz
mostly recorded
during wakeful states
Beta Wave (Wikipedia
of the brain. This is
2016c)
seen when the brain
is alert; during motor
functions, problem
solving and active
concentration.
Gamma
30Hz –
This is the fastest
100Hz
brain wave. It’s
gotten from a much
Gamma Wave (Wikipedia
localized part of the
2016c)
brain, and is mostly
associated with a
high level of
information
processing, and
meditation.
Mu
8Hz – 13Hz
This overlaps the
Alpha frequency
range. This is also
Mu Wave (Wikipedia
called the
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Igberaese 18
Sensorimotor
2016c)
Rhythm. This occurs
in the motor cortex of
the brain.
2.5 Noise, Artifacts and Artifacts Reduction
The voltage range of EEG signals recorded from the scalp is usually between 10 –
100 µV. This makes the signal susceptible to electrical interferences, also called artifacts.
Artifacts contaminate the EEG signals and make it hard to discern what the real signals
are.
Artifacts can be generated from parts of the body other than the brain. Such
artifacts are referred to as physiologic or biological artifact. Artifacts that are generated
from sources other than the body are referred to as extraphysiologic or environmental
artifacts. Some of such sources are machines nearby (Benbadis and Rielo 2015).
Biological artifacts commonly occur due to eye movements, such as a blink. They
are due to the potential difference between the cornea and the retina, which is larger than
potential difference in the Cerebrum. Other similar artifact sources are the heart, which
produces the Electrocardiographic (ECG) artifacts; the muscles that are responsible to
Electromyographic (EMG) artifacts; and the tongue producing a glossokinetic artifact.
The glossokinetic artifact occurs because of the potential difference between the base of
the tongue and the tip of the tongue (Benbadis and Rielo 2015).
Environmental artifacts can be recorded due to the EEG electrodes. This is related
to the electrode popping, which happens as an electrode settles on the scalp. This
phenomenon usually causes a sudden change in impedance. Environmental artifact may
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also be caused by alternating currents. This becomes a problem when the impedance of
an electrode is larger than the ground of the amplifier. Movement of persons around EEG
subject may also cause artifacts. This is usually because of capacitive or electrostatic
effects between two people, whose bodies act as conductors.
Artifacts are a serious consideration when processing EEG signals because they
can easily distort the real signal, making it difficult to extract relevant information for
eventual processing. Artifact removal is usually the first step taken towards Signal
processing.
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Chapter 3
Experiment Design
3.1 Background
This experiment was designed as a short-term memory test based a common
complaint from Autism sufferers that they are not able to retain short-term memory.
Short-term memory refers to the resources of the human faculty that can hold a restricted
measure of data in an exceptionally open state for a short time (Cowan 2009, 323-338).
Short-term memory can be compared to a buffer in a computer system and is particularly
useful in carrying out day-to-day activities. For example, when listening to a person
telling a story, short term memory is used to hold initial parts of the story till it is
complete. In this instance, a person can then have full comprehension of the overall
situation.
Different scientists have different definitions and concepts of what short-term
memory is. James Williams called short-term memory the Primary memory. He believes
for memory to be termed what it is, it must have endured for a certain length of time; it is
in a substantive state. Such memory is only associated with that moment or circumstance
(James 1890). Donald Broadbent, a psychologist, refers to short-term memory as
Immediate memory; it is usually present in an instance of the brain trying to filter
messages from multi-channels, but useless as other messages were detected by the brain
(Broadbent 1958).
Richard Atkinson and Richard Shiffrin, differentiate long- and short-term memory
in the way they are structured, and in their process control. They argue that short-term
memory is stored in a short-term store. The information stored in this store will decay
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with time; however, they could not say how long it would take. Long-term memory is
stored more “permanently”, than the short-term memory (Atkinson and Shiffrin 1968, 89195).
Short-term memory is usually stored in the frontal lobe of the brain; the prefrontal cortex is the most active part of the brain during this exercise. Other parts of the
brain, such as the hippocampus, are active when short-term memory consolidates to longterm memory. For this project, we were considering EEG signals recorded by electrodes
placed on the frontal lobe region of the scalp.
3.2 Experiment Setup
Six children participated in this research. There were no specific criteria to select
candidates for the procedure; most of the children consulted with the Education
Department at Lamar University, so we simply worked with which ever children were
present. The children are between 9 and 16 years old, with 4 boys and 2 girls. They
comprised of 3 autisic subjects and 3 control subjects.
We used the Emotiv Epoc headset to collect EEG for these experiments. The
Emotive EPOC EEG neuro-headset is a portable EEG machine with a 16 electrodes
placed according to the 10-20 standard system. The headset was designed such that it can
fit any size of scalp. This was suitable for the purpose of this experiment because the
headset was unattached and portable making the whole process uncomplicated for the
children. The Emotiv Epoc headset has 16 electrodes; 14 electrodes to collect relevant
EEG data, and two electrodes used as the reference electrodes.
The experiments were carried out in the Applied DSP lab; and at Lamar
University Education Department, where some of the children had come for further
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evaluation and testing with the Education department towards special learning
techniques. Figure 14 below illustrates the placement of electrodes
Figure 8. 14 Channel electrode placements, including the two reference electrodes CMS
and DRL (Gmac 2009).
3.3 Experiment Procedure
For this experiment, subjects were shown 10 images. They were a mix of pictures
that were familiar, and a few of the images were unknown to spark their curiosity. Each
image was displayed for 5 seconds before it was replaced by another picture. After
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viewing all 10 pictures, the subjects were asked to close their eyes for 5 minutes, and then
open them to stare at a dot for another 5 minutes. They were then asked to recall the
pictures they saw. The EEG signal of interest was extracted from the duration of time it
takes to recall the pictures.
After the first few experiments, we modified the duration of waiting to recall the
pictures to 6 minutes instead of 10 minutes. The reason for this was that children were
impatient with having to sit and wait for that long. The images we used for this procedure
are shown below in figures 9 to 18:
Figure 9. Ellen H. Swallow Richards House Boston MA (Wikipedia 2009)
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Figure 10. A Bright Orange Gazania flower in Full bloom (Wikipedia 2004)
Figure 11. Brown Kenneth Bear Plush Toy
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Figure 12. President Barack Obama
Figure 13. Volkswagen Passat
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Figure 14. An oak tree
Figure 15. An orange
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Figure 16. A mathematical equation
Figure 17. A cute baby
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Figure 18. Arizona sunset
The images used in the experiment were obtained from the Internet and were
projected on the screen approximately 2 meters in front of the participants.
3.4 Data Acquisition
The experimental equipment were setup before displaying pictures. This gave us
enough time to test for connectivity and to make sure that the EEG data were being
recorded, while the experiment was going on. The Test Bench software, which works
with the Emotiv Epoc headset, was used to record and monitor connectivity during the
experiment. It also saved the resulting data for further analysis.
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Chapter 4
Signal Analysis
4.1 Introduction
After collecting EEG data of all 6 subjects, we worked with only 2 EEG signals
out of the 6 recorded. This is because the EEG signals from these 2 participants were
from the only subjects who were suitably matched in age and sex. The subjects are both 9
year old males.
4.2 EEG Analysis Tools
We used the following software for signal analysis:

MATLAB: this is a graphic interface software used to perform engineering
computations. This was the software primarily used in this project. It was the
platform, from which every other software ran.

EEGLAB: this is a MATLAB toolbox used specifically for processing EEG
data. This was used only to help understanding and visualization of the raw
EEG, as well as to carry out preprocessing of the EEG signals.
4.3 EEG Preprocessing
EEG preprocessing is a process where the raw EEG signal is prepared for further
investigation and feature extraction. This process is necessary because during the process
of recording the data, noise, also known as artifacts, is acquired. These artifacts, which
are very similar to the EEG signal, can distort the EEG signal and eventually affect the
end result of the signal analysis. The methods used in the preprocessing stage of signal
analysis were chosen based on the kind of artifact that the EEG signal may have acquired.
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4.3.1 Common Average Reference
In recording EEG activities in the brain, finding a part of the human body where
electrical activities are considered neutral is a common problem. In EEG recording, each
electrode is expected to record electrical activity precisely at the site where the electrode
is located. However, voltage is measured relative to another site that is usually called the
reference site (Alhaddad et al. 2012).
In the instance of this EEG recording, the Emotiv Epoc EEG recording tool has
16 electrodes; two of the 16 electrodes are there as referencing electrodes. The
implication of this is that the during the EEG recording, voltage at the referencing
electrodes may contribute to the resulting electrical recording of the recording electrode.
In order to eliminate this noise, a Common Average Reference is evaluated for all
the electrodes by re-referencing EEG signal, while converting data from a fixed reference
to the average reference. The common average reference is achieved by making the
reference for each electrode null or zero (SCCN 2011).
In Common Average Referencing, the average value of the samples recorded is
subtracted from each recorded sample. It can be calculated as
𝑚
𝐿𝐶𝑜𝑚𝑚𝑜𝑛 𝐴𝑣
1
= 𝐿𝑓𝑖𝑥𝑒𝑑 − ∑ 𝐿𝑖
𝑚
1
where Lfixed is the reference of the EEG signal from individual electrodes; and m
is the total number of electrodes.
4.3.2 DC Offset Removal
In preparing for the experimental setup, the Epoc electrodes are drenched with
saline solution to make them readily conduct electricity and detect EEG signals. This
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process creates an electrical potential difference as a result of flow of ions from the
surface of the electrodes. The potential difference generated at the electrode usually
remains, and adds a DC offset voltage at the input of the EEG amplifier. The DC offset
voltage can be very large when compared to the magnitude of electrical activities from
the brain (Niedermeyer et all 2011). The DC offset needs to be removed to obtain the true
voltage amplitude of the EEG signal.
The DC offset is evaluated as the mean value of a time varying signal, such as an
EEG signal, over a period of time (Campbell 2015). Ideally, the negative and positive
part of the DC offset cancels out resulting is zero volts. However, this is not usually the
case, and the DC offset ends up increasing the baseline of the EEG signal to the DC
voltage level.
To remove DV offset from the signal, the mean value of the signal is subtracted
from the signal. DC offset is calculated as:
𝑛
1
𝑉0 = 𝑉𝐷𝐶 − ∑ 𝑉𝑖
𝑛
1
where VDC is the signal with DC offset; n is the total number of samples.
4.3.3 Notch Filtering
EEG signals are often contaminated by artifacts from wires and other electrical
equipment in the room where EEG testing is conducted. This artifact, also known as
Powerline noise, usually has a frequency of 60 Hz, and their harmonics, such as 120 Hz,
180 Hz, and so on.
The Nyquist-Shannon Sampling theorem states that the sampling rate must be at
least twice the highest waveform frequency of interest. This means that at a sampling rate
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of 128 Hz (which is the default value for Emotiv Epoc), the highest frequency of signal
we can analyze will be:
𝑓𝑁𝑦𝑞𝑢𝑖𝑠𝑡 =
1
𝑣
2
where v is sampling rate.
In applying the notch filter, also known as a band-reject filter, to the EEG signal
we can design a filter suppressing 60 Hz, as this is the only frequency component in its
harmonics that will be contained in the EEG signal.
4.3.4 Half Second Segmentation and DC Offset Removal from Half Second Fragments
After EEG signal was notch filtered, the signal was segmented into half-second
fragments, for further processing. This is because EEG signals are non-stationary in
nature (Klonowski 2009. This is done so that “stationary” data can be further processed.
In this project, we worked with 10-seconds of EEG signals recorded as the subject tried
to recall the images shown in the beginning of the experiment.
After fragmentation of the 10-second long signal, DC offset voltage is removed
from each half second fragment. DC offset is removed from each half fragment by
subtracting the mean value of the signal from the original signal. It is important to note
that the original signal referred to here is the already fragmented signal, and not the signal
before fragmentation occurred.
DC offset was eliminated according to the follows:
𝑛
𝑉0(𝐻𝑎𝑙𝑓 𝑠𝑒𝑔)
1
= 𝑉𝐷𝐶(𝐻𝑎𝑙𝑓 𝑆𝑒𝑔) − ∑ 𝑉𝑖
𝑛
1
where VDC(Half seg) is the half segment fragmented signal with DC offset
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4.4 EEG Feature Extraction
EEG signals are electrical signals in time domain. Signals in this state are nonlinear and non-stationary (Klonowski 2009). In addition to these properties, the signals
are buried in noise, and the energy of signal is scattered. This makes the signal difficult
to analyze. The features of the EEG signal collected from each subject contain
information specific to that subject, making it possible to distinguish between autistic and
control subjects. To analyze the features in these signals, these features need to be
extracted (Suleiman and Fatehi 2016).
Feature extraction may be performed by various methods, based on the feature of
interest. Extracted features can either be in time and/or in frequency domain. Mental
tasks are best recognized when EEG is analyzed in frequency domain (Kumar and
Kareemullah 2014). For the purpose of this experiment, we will be performing feature
extraction by the Modified Covariance Method. This technique was selected because of
the non-stationary property of EEG samples.
4.4.1 Power Spectral Density
The objective of EEG feature extraction is to extract features that would be used
to distinguish between autistic and non-autistic subjects. Power spectral density (PSD)
estimates were evaluated for this purpose.
Power Spectral Density (PSD) estimates describes how the power of a signal is
distributed over frequency. It shows the strength of energy of a signal, as a function of
frequency (Wikipedia 2016f). The PSD estimates are useful, and particularly in this
thesis, because they show the weak and strong variations of frequency. The unit of PSD
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is energy per frequency; energy can be obtained within a certain frequency range by
integrating PSD within that frequency range (Cygnus Research International 2016).
Since PSD estimates are usually evaluated over a frequency range, we will be
evaluating PSD estimates for frequencies up to 64 Hz. This is because, according to the
Nyquist-Shannon sampling theorem, the sampling rate must be at least twice the highest
waveform frequency of interest. Therefore, if the sampling frequency of the Emotiv Epoc
headset is 128 Hz, the EEG signal can only be evaluated for frequencies up to 64 Hz. We
used the Modified Covariance method to evaluate the PSD estimate of the EEG Signal.
4.4.1.1 Modified Covariance Method
Because the DFT-based methods of PSD estimation are likely to produce biased
results for short sequences, the Modified Covariance autoregressive model was used to
evaluate PSD estimates of the EEG fragments.
The Modified Covariance method is a parametric spectral estimation technique
used to evaluate the autoregressive power spectral estimate of a signal. This is a
modification of the covariance method of spectral estimation. The Covariance method
finds the autoregressive model that minimizes the sum of the squares of the forward
prediction error; but the modified covariance method minimizes the sum of the squares of
both the forward and backward prediction errors.
In other to evaluate the PSD estimate, the appropriate order of the Autoregressive
model should be selected. The model order can be determined by a number of criteria:
Akaike information criterion, Bayes factor, Bayesian information criterion, Deviance
information criterion, False discovery rate, Focused information criterion, Mallows's Cp,
Minimum description length (Algorithmic information theory), Structural Risk
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Minimization and Stepwise regression. The most commonly used are the Akaike
information criterion (AIC) and the Bayes factor and/or the Bayesian information
criterion (Wikipedia 2016d)
Let us assume that there is a true model that has generated the time series, in our
case, the EEG signal. This true model is infinitely dimensional, in nature. However,
because we seek to use an autoregressive model, we must find a finite-order
autoregressive model to approximate the true model. In 1973, Akaike proposed the
Akaike information criterion, AIC, which provides an unbiased estimate of the distance
between the fitted AR model and the true model (Fabozzi et al. 2014, 399-403).
In selecting a model order of auto regressive models, Simpson and colleagues
conclude that there is no “correct” order for real data. This is because the variation from
epoch to epoch is far larger than any change introduced by increasing model orders in the
range of 10 to 40 (Simpson et al. 2016). However, McFarland and colleague conclude in
their experiment that lower frequency signals require higher model orders. Increasing the
model order and decimating the signals were effective in increasing spectral resolution
(Mcfarland and Wolpaw 2008). Florian and colleague suggested that the optimal model
order for spectral analysis of EEG signal was 11 with small differences with order values
between 9 and 13 (Florian and Pfurtscheller 1995). These experiments conclude that
model order of 11 will be suitable for EEG signal; with sampling frequency of 128 Hz,
such as is the case in this project.
4.4.2 Power Spectral Density Estimates for Each Half Second Segment
PSD estimates for each half-second fragments were evaluated after deciding on
using the modified covariance method with the model order of 11. PSD estimates were
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evaluated for 20 half-second fragments and for each EEG channel of both the autistic and
control groups. This resulted in a large number of vectors representing PSD estimated for
all 20 half-seconds of 14 EEG channels. Average PSD estimates were evaluated, and
used as datasets for the statistical analysis and classification.
4.5 Kruskal-Wallis Test
The Kruskal-Wallis test is a non-parametric test that is used to determine the
statistical difference between two independent groups of data. It determines the degree to
which independent samples are associated, by assuming the samples are in the same
distribution. Compared to the parametric equivalent of the Kruskal-Wallis test, the One
Way Analysis of Variance (ANOVA), the Kruskal-Wallis test does not assume that data
samples have normal distribution.
This method is named after Dr. Williams Kruskal and Dr. W. Allen Wallis, two
American mathematicians. The Kruskal-Wallis test is an extension of the Mann-Whitney
U test that enables analyzing more than two samples.
The Kruskal-Wallis test is computed by comparing the data mean ranks (median),
instead of the data means themselves. The test is expected to return a null hypothesis that
the medians for the different independent data sets are equal (Bewick, Cheek and Ball
2004).
This test is evaluated based on the assumptions that the dependent samples are
measured at the ordinal level; that each independent sample has two or more independent
groups; and that there is independence in observations (Laerd Statistics 2016).
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The Kruskal-Wallis test reports a p-value, which is the probability of finding the
observed result, when the null hypothesis of an experiment is true. In simple terms, this
will mean that the independent data sets originate from the same distribution. The p-value
could also indicate that the alternative hypothesis, which is the opposite of the null
hypothesis, is true (StatsDirect Limited 2016).
Whether the null hypothesis or the alternative hypothesis is true is determined by
if the p-value reported is greater than, equal to, or less that the threshold value. If the pvalue is equal to or less that the threshold value, it is interpreted as strong evidence
against the null hypothesis and the null hypothesis is rejected. However, if the p-value is
greater than the threshold value, the evidence against the null hypothesis is weak, and so
we accept the null hypothesis. A generally accepted threshold value is 0.05; therefore, the
threshold value of 0.05 was used in this project.
The Kruskal-Wallis test was evaluated on the averaged PSD estimated from EEG
signal of autistic and control subjects, after which the K-Nearest Neighbor (KNN)
classifier using the Euclidean distance was used to classify the average PSD estimates.
4.6 KNN Classification
The KNN classification is a non-parametric method of classification that uses
pattern recognition. It uses an instance-based learning method, and classifies based on the
closest neighbor. The nearest neighbor of a data sample is defined by the distance
measures, such as Euclidean distance, Hamming distance, Chebychev distance, etc.
K-NN uses a simple algorithm to classify data, and kinds the nearest neighbor of
“k” variables in the feature space, where “k” is the number of variables of interest. It is
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important to select a high value of k. This is because the training data is locally sensitive,
and is susceptible to noise. A high value of k ensures that k-results are smoother. For this
project, “k” value is 9.
When using a K-NN classifier deciding how to split the feature space into testing
and training data sets can be a problem. I used the leave-one-out cross-validation method
for this purpose. Cross-validation is a method of comparing two data sets, such as the
training and testing data sets. It is sometimes called rotation estimation, and is used to
access how results of a statistical analysis will generalize to an independent data set.
There are various types of cross-validation such as k-fold cross validation, resubstitution
validation, hold-out validation, leave-one-out cross-validation, etc. (Wikipedia 2016e).
In the leave-one-out cross validation, one observation is considered the test data,
and the remaining data as the training data set. This is repeated till every data in the
feature space is exhausted. The leave-one-out cross validation is most preferred because
there is no chance of overlapping training set with test data at any point in the validation.
It takes a lot of time to process but it is very exhaustive.
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Chapter 5
Result and Discussion
5.1 Power Spectral Density
After preprocessing, we chose to work with a 10-second segment of the total EEG
data acquired. This 10-second time signal was extracted from the portion of the EEG
signal recorded when the experiment subjects was tasked with recalling images viewed
during the experiment. The 10 second segment was them fragmented into half-second
fragments. This resulted into 20 half-second fragments. This is so that we can have
stationary data to work with. PSD estimates of all 20 half-second fragments were
evaluated using the modified covariance method. For this experiment, we used AR model
order 11. Figure 19 shows a plot with all 20 PSD estimate from one of the EEG
channels, for one of the subjects.
Figure 19. PSD plot of one electrode
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The resulting PSD estimates from each channel were averaged. The result is an
average PSD estimate for each EEG channel. Figures 20 to 33 below are plots showing
the average PSD estimates for each EEG channel and for the autistic and control subjects.
They have been juxtaposed against the other for comparison.
Figure 20. Averaged PSD estimates for AF3 EEG electrode and for autistic and control
participants
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Figure 21. Averaged PSD estimates for AF4 EEG electrode and for autistic and control
participants
Figure 22. Averaged PSD estimates for F3 EEG electrode and for autistic and control
participant
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Figure 23. Averaged PSD estimates for F4 EEG electrode and for autistic and control
participants
Figure 24. Averaged PSD estimates for F7 EEG electrode and for autistic and control
participants
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Figure 25. Averaged PSD estimates for F8 EEG electrode and for autistic and control
participants
Figure 26. Averaged PSD estimates for FC5 EEG electrode and for autistic and control
participants
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Figure 27. Averaged PSD estimates for FC6 EEG electrode and for autistic and control
participants
Figure 28. Averaged PSD estimates for O1 EEG electrode and for autistic and control
participants
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Figure 29. Averaged PSD estimates for O2 EEG electrode and for autistic and control
participants
Figure 30. Averaged PSD estimates for P7 EEG electrode and for autistic and control
participants
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Figure 31. Averaged PSD estimates for P8 EEG electrode and for autistic and control
participants
Figure 32. Averaged PSD estimates for T7 EEG electrode and for autistic and control
participants
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Figure 33. Averaged PSD estimates for T8 EEG electrode and for autistic and control
participants
The plots illustrated in figure 20-33 show differences in power spectrum between
the EEG collected from autistic and control individuals. We observe that the averaged
EEG power is higher for autistic subject from 0 Hz to 16 Hz, while the averaged EEG
power is higher for the control subject between 16 Hz and 28 Hz for electrodes F4, F8, FC5
and AF4. On the other hand, for the electrodes F3, F7, T7 and T8, the control subject shows
higher EEG power between 13 Hz and 30 Hz. For the electrodes and FC6 and AF3, the
control aubject has slightly higher power than the autistic subject between 15 Hz and 43
Hz. Lastly, for EEG channels O1, O2, P7, P8, and F8, no significant difference in power
between autistic and control individuals is observed.
In general, we observe that the PSD estimates of control subject is more than the
PSD estimates for autistic subject within the 13Hz and 29Hz for EEG channels AF3, AF4,
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F3, F4, F7, F8, FC5, FC6, T7, and T8. The channels with the most difference are F3, F7, T7,
T8, FC6 and AF3. All of these EEG channels are located on the area of the scalp just
above the frontal lobe of the brain, as discussed earlier in this project. The frequency
range where the most difference between PSD estimates of control and autistic subject is
recorded also fall within the Beta frequency wave range (15Hz and 30Hz). This is the
frequency range mostly associated with problem solving and active concentration
(Wikipedia 2016c). Research has indicated a statistically significant increase in average
frequency of beta frequency wave, in the exercise of recalling short term memory (Binder
et al. 2012). The results show that control subject EEG signal has higher power.
5.2 Kruskal-Wallis Test
After the averaged PSD estimates were produced, the Kruskal-Wallis test
compares the average PSD estimates vectors between the autistic and control subjects.
The PSD estimates were ranked to determine if both estimates belong to the same
statistical distribution.
For this project, the null hypothesis is that the PSD estimates of the two subjects
stem from the same statistical distribution. The alternative hypothesis is that the PSD
estimates come from two different statistical distributions, and hence are unique from
each other. The latter would indicate a statistically significant difference in EEG between
the autistic and control subjects that could be used in attempting an EEG-based detection
of autism.
The significance level for this test was assumed as 0.05. Therefore, if the KruskalWallis test returns a p-value less than 0.05, the null hypothesis is rejected, and the
alternative hypothesis, accepted. This will indicate that there is significant statistical
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difference between autistic and control brain waves, indicating that they are from
different groups. However, if the test returns a p-value greater than 0.05, the null
hypothesis is accepted, and there will be no statistical difference between autistic brain
waves and control brain waves. The table 2, below, shows the results from the KruskalWallis test, carried out on each electrode.
The p-values from the Kruskal-Wallis test show that all 10 out of 14 channels are
below 0.05, which is our significant value. This means that the null hypothesis is rejected,
and the alternative hypothesis is accepted, indicating that EEG signal from autistic and
control subject are from the different statistical distribution.
5.3 K-NN Classification
For this project, the KNN classifier was used to differenticate between the autistic
and control subjects based on their EEG extracted features. We used the leave-one-out
cross validation for this purpose. The K-value for this classification was set to 9. After
evaluating the K-nearest neighbor, the percentage of correctly classified features was
evaluated. The result showed that 89.29% of the extracted features were accurately
classified, suggesting that 10.71% of the features extracted from both autistic and control
were misclassified.
The result indicates that autistic and control features are clearly different from
each other, and EEG signals can be used as a biomarker to differentiate between the brain
children diagnosed with autistic and healthy brain
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Table 2. P-values from Kruskal-Wallis Test
EEG Channel P-value from KW test
AF3
3.35e-06
F7
9.15e-07
F3
7.29e-04
FC5
0.1772
T7
1.65e-14
P7
0.0138
O1
0.1518
O2
3.79e-05
P8
0.4996
T8
9.40e-18
FC6
1.91e-08
F4
0.0503
F8
5.17e-04
AF4
0.0127
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Chapter 6
Conclusion and Future Work
6.1 Conclusions
This pilot study explored the possibility of using EEG as a biomarker to diagnose
Autism. For this purpose, we recorded EEG signals from 3 autistic subjects and 3 healthy
subjects, while they viewed a slide show of 10 pictures, during 6-10 minutes of closing
and opening their eyes, and about 15 seconds of subjects tasked with recalling the
pictures viewed. The period of recall was most of interest, as this work is aimed at
comparing EEG signals as the brain tried to recall short-term memory.
After signal preprocessing, we chose to work with a 10- second fragment of the
EEG signals recorded of when the subjects recalled the images viewed during the
experiment. We, then, proceeded to fragment this 10 second segment into 20 half-second
fragments. This was done to be able to work with stationary data.
EEG features were extracted by evaluating the Power Spectral Density estimates,
of all 20 of the half second fragments, using the modified covariance method. PSD
estimates were evaluated for all channels. The PSD estimate plots show the different
frequency bands that distinguish autistic subject from control subject. The PSD estimates
show that EEG signal from control subject have more average power than autistic subject
between the Beta frequency wave on electrodes located on scalp above the frontal lobe of
the brain. This is particularly significant as this is the part of the brain engaged in
recalling short term memory.
Kruskal-Wallis test was evaluated on the PSD estimates from autistic and control
subjects. We used a significant level of 0.05. Ten out of 14 channels returned a p-value
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Igberaese 52
less than the significant level in the Kruskal-Wallis test. This showed that the EEG
signals from autistic and control subjects are from different statistical distribution, and
are statistically different; autistic and control PSD estimates belong to different groups.
Finally, we performed classification of averaged PSD estimates from autistic and
Control subjects, using a K-Nearest Neighbor (KNN) classifier. We used the Leave-OneOut cross-validation method, with a k-value of 9. The classifier returned a result of
89.29% accuracy. From the result, we may conclude that EEG signal can be used a
biomarker to diagnose Autism.
One major limitation of this experiment is that we couldn’t adequately pair our
subjects into autistic and control groups. We had 3 autistic subjects (all males, 9-years,
15-years and 16-years old) and 3 control subjects (1 male, 9-years old; and 2 females, 9years and 13-years old). We were only able to work with EEG data of the two 9 year-old
males, as they were the only ones who were of the same sex and age. We were reluctant
to work with any other pairs as they would either be of different sex or age.
6.2. Future Work
One obvious improvement to this work is to include EEG from a bigger and,
perhaps, more diverse groups of ASD and control participants. This would mostly likely
give a more conclusive result .Another modification would be to consider other EEGbased metrics, such as coherence or entropy. Perhaps, incorporating other metrics would
contribute to a better classification.
Finally, in this experiment, we worked with EEG recorded during the memory
test. Other experiments could focus on any of the following:
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1. EEG can be recorded as children were looking at different patterns and analyzed
for differences in how they respond to the different samples;
2. Perhaps, other types of stimulations, such as various colors shown, eye
coordination tasks, etc., may be used to evoke different responses from ASD and
control subjects.
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Igberaese 54
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