analysis of pre-ictal and non-ictal eeg activity: an emotiv and …/67531/metadc955040/... ·...
TRANSCRIPT
APPROVED: Vijay Vaidyanathan, Major Professor Kamesh Namuduri, Committee Member Xinrong Li, Committee Member Shengli Fu, Chair of the Department of
Electrical Engineering Costas Tsatsoulis, Dean of the College of
Engineering Victor Prybutok, Vice Provost of the Toulouse
Graduate School
ANALYSIS OF PRE-ICTAL AND NON-ICTAL EEG ACTIVITY:
AN EMOTIV AND LabVIEW APPROACH
Oscar Ferney Medina
Thesis Prepared for the Degree of
MASTER OF SCIENCE
UNIVERSITY OF NORTH TEXAS
December 2016
Medina, Oscar Ferney. Analysis of Pre-ictal and Non-ictal EEG Activity: An EMOTIV and
LabVIEW Approach. Master of Science (Electrical Engineering), December 2016, 67 pp., 2 tables,
38 figures, 26 numbered references.
In the past few years, the study of electrical activity in the brain and its interactions with
the body has become popular among researchers. One of the hottest topics related to brain
activity is the epileptic seizure prediction. Currently, there are several techniques on how to
predict a seizure; however, most of the techniques found in research papers are just
mathematical models and not system implementations. The seizure prediction approach
proposed in this thesis paper is achieved using the EMOTIV Epoc+ headset, MATLAB, and
LabVIEW as the analog and digital signal processing devices. In addition, this thesis project
incorporates the use of the Hilbert Huang transform (HHT) method to obtain intrinsic mode
functions (IMF) and instantaneous frequency components of the transform. From the IMFs,
features as variation coefficient (VC) and fluctuation indexes (FI) are extracted to feed a support
vector machine that classifies the EEG data as pre-ictal and non-ictal EEGs. Outstanding
patterns in non-ictal and pre-ictal are observed and demonstrated by significant differences
between both types of EEG signals. In other words, a classification of EEG signals according to a
category can be achieved proving that an epileptic seizure prediction technology has a future in
engineering and biotechnology fields.
ii
Copyright 2016
by
Oscar Ferney Medina
iii
ACKNOWLEDGEMENTS
It has been quite an honor to work with Dr. Vijay Vaidyanathan for the past two and a
half years. He has not only provided me with the opportunity to work with him in my research,
but also be able to be part of the creation of the biomedical engineering department at the
University of North Texas. He has also served as a mentor and a friend. Also, I would like to give
thanks to Dr. Kamesh Namuduri for being patient with me throughout my time in the electrical
engineering department and for providing the means to have a partnership with the biomedical
and electrical engineering departments. Thanks to Ramanpreet Singh for being my
unconditional friend and study partner. Without him, graduate school would have been a lot
harder. Last, I would like to thank my parents, Adela and Oscar, and my sister Jennifer for being
there for me in the most difficult times and providing their unconditional support.
iv
TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS ...................................................................................................................iii LIST OF TABLES ............................................................................................................................... vii LIST OF FIGURES ............................................................................................................................ viii CHAPTER 1. INTRODUCTION ........................................................................................................... 1
1.1 Motivation ............................................................................................................... 1
1.2 Electroencephalography (EEG) ............................................................................... 2
1.3 Epilepsy and Seizure Basics ..................................................................................... 3
1.3.1 Common Generalized Seizures [1] .............................................................. 4
1.3.2 Common Partial or Focal Seizures [1] ......................................................... 4
1.3.3 EEG Activity Terminology ............................................................................ 5
1.4 Artifacts ................................................................................................................... 5
1.5 Conclusion ............................................................................................................... 6 CHAPTER 2. METHODS .................................................................................................................... 7
2.1 EEG Acquisition device ............................................................................................ 7
2.1.1 Other EMOTIV Epoc+ Specifications ........................................................... 8
2.1.2 EMOTIV Pure EEG Raw EEG Software ......................................................... 8
2.2 Software .................................................................................................................. 9
2.2.1 Simulink ....................................................................................................... 9
2.2.2 MATLAB ..................................................................................................... 10
2.2.3 LabVIEW .................................................................................................... 11
2.3 Mathematical Background .................................................................................... 12
2.3.1 The Fourier Transform .............................................................................. 12
2.3.2 The Discrete Fourier Transform (DFT) ...................................................... 13
2.3.3 The Fast Fourier Transform (FFT) .............................................................. 13
2.3.4 Fourier Transform Digital Implementation ............................................... 14
2.3.5 Hilbert Huang Transform (HHT) ................................................................ 14
2.3.6 Empirical Mode Decomposition ............................................................... 14
v
2.3.7 The Hilbert Transform ............................................................................... 17
2.3.8 Instantaneous Frequency ......................................................................... 17
2.3.9 Statistical Mean ........................................................................................ 18
2.3.10 Standard Deviation ................................................................................... 19
2.3.11 Feature Extraction ..................................................................................... 20
2.3.12 Variation Coefficient ................................................................................. 21
2.3.13 Fluctuation Index (FI) ................................................................................ 21
2.3.14 Machine Learning Algorithms for Classification ....................................... 23
2.3.15 Machine Learning Algorithms ................................................................... 24
2.3.16 Supervised Learning .................................................................................. 24
2.3.17 Support Vector Machine ........................................................................... 25
2.3.18 Linear Classification SVM .......................................................................... 26
2.3.19 Nonlinear Classification SVM .................................................................... 27
2.4 LabVIEW Code ....................................................................................................... 28
2.5 LabVIEW Code List ................................................................................................ 29
2.5.1 EMOTIV Epoc+ to LabVIEW ....................................................................... 29
2.5.2 LabVIEW EMOTIV Toolkit V2 ..................................................................... 30
2.5.3 EMOTIV Toolkit VI Modifications .............................................................. 30
2.6 Hilbert Huang Transform and EMD ...................................................................... 35
2.6.1 Feature Extraction ..................................................................................... 36
2.6.2 Support Vector Machine: Supervised Learning ........................................ 38
2.7 Conclusion ............................................................................................................. 39 CHAPTER 3. RESULTS AND DISCUSSION........................................................................................ 41
3.1 EEG Signal LabVIEW Reader .................................................................................. 41
3.2 Non-ictal and Pre-Ictal EEG Signal Analysis .......................................................... 42
3.3 EEG Signal Visual Inspection ................................................................................. 42
3.4 EMD and Features ................................................................................................. 45
3.5 SVM Training Data Set Analysis ............................................................................ 45 CHAPTER 4. COMMENTS, FUTURE WORK, AND CONCLUSION .................................................... 53
4.1 The Next Generation ............................................................................................. 53
vi
4.2 Other Considerations ............................................................................................ 53
4.3 Future Work .......................................................................................................... 54
4.3.1 A Doctor's Recommendation .................................................................... 54
4.3.2 A Patient’s Perspective ............................................................................. 55
4.3.3 Personal Comments .................................................................................. 58
4.3.4 Emergency Response Protocol ................................................................. 59
4.3.5 A Mobile Phone Application ..................................................................... 60
4.3.6 Adaptation of an EMOTIV Headset ........................................................... 60
4.4 Conclusion ............................................................................................................. 61 REFERENCES .................................................................................................................................. 64
vii
LIST OF TABLES
Page
3.1 Features extracted using the EMD VI LabVIEW code ....................................................... 46
3.2 Differences between non-ictal and pre-ictal features ...................................................... 50
LIST OF FIGURES
Figure 1: EEG waves by frequency [1]............................................................................................. 2
Figure 2: EMOTIV Epoc+ and Insight EEG headsets. Courtesy of EMOTIV Brainware® ................. 3
Figure 3: EPOC headset Channel Position Map. Courtesy of EMOTIV Brainwear®. ....................... 8
Figure 4: EMOTIV Xavier Composer-Raw EEG Software. ................................................................ 9
Figure 5: EMD Data, Mean, Upper and Lower Envelope .............................................................. 16
Figure 6: EEG IMF1 though IMF5 example ................................................................................... 17
Figure 7: Prediction Algorithm Flow Chart ................................................................................... 23
Figure 8:SVM Support Vectors and Separating Hyperplane ........................................................ 27
Figure 9: Nonlinear SVM example Data set [24]........................................................................... 28
Figure 10: EMOTIV LabVIEW Toolkit VI (original Version) [25] .................................................... 30
Figure 11: Original Plot EEG sub VI ............................................................................................... 31
Figure 12: Modified Plot EEG VI .................................................................................................... 31
Figure 13: Front Panel EEG Signal Reader .................................................................................... 33
Figure 14: Case Structure Example Help ....................................................................................... 33
Figure 15: Modified EEG Reader with Case Structure .................................................................. 33
Figure 16:Modified EEG Reader with Case Structure Part 1......................................................... 34
Figure 17:Case Structure to Save 512 Recorded Samples ............................................................ 35
Figure 18: Hilbert Huang Transform VI ......................................................................................... 36
Figure 19: Fluctuation Index and Coefficient of Variation Extraction [26] ................................... 37
Figure 20:Front Panel for EEG readings, IMFs, VC and FI ............................................................. 38
Figure 21 SVM LabVIEW code Block diagram ............................................................................... 39
viii
Figure 22: EEG Signal LabVIEW Reader Front Panel ..................................................................... 41
Figure 23: Ten Minute Non-Ictal EEG Reading.............................................................................. 42
Figure 24: Ten Minute Non-Ictal EEG Reading Continued ............................................................ 43
Figure 25:Ten Minute Non-Ictal EEG Reading Final ...................................................................... 43
Figure 26:Ten Minute Pre-Ictal EEG Reading ................................................................................ 44
Figure 27:Ten Minute Pre-Ictal EEG Reading Continued .............................................................. 44
Figure 28:Ten Minute Pre-Ictal EEG Reading Final Section .......................................................... 44
Figure 29: EMD Front Panel with EEG, IMFs, and Features Display ............................................. 45
Figure 30:Variation Coefficient: Non- Ictal and Pre-Ictal Values .................................................. 47
Figure 31:Fluctuation Index: Non-Ictal and Pre-Ictal Values ........................................................ 48
Figure 32 Variation Coefficients for Ictal and Inter-ictal EEG segments....................................... 48
Figure 33 Fluctuation Index for Ictal and Inter-Ictal EEG segments [9]. ....................................... 49
Figure 34:Variation Coefficient Vs Fluctuation Index Plot ............................................................ 49
Figure 35:SVM Refined Training Data Plot ................................................................................... 51
Figure 36: SVM Classifier Test 1 .................................................................................................... 52
Figure 37:SVM Classifier Test 2 ..................................................................................................... 52
Figure 38: Block Diagram Cellphone App Feature ........................................................................ 60
ix
CHAPTER 1
INTRODUCTION
1.1 Motivation
The study of EEG signals is not novel and there are numerous applications in which EEG
signals can help us understand and develop new sophisticated technology. Studies using EEGs
have been used for biomedical applications as well as military applications; however, there is not
a unique or set of patterns that allow us to fully understand the nature of EEG signals. In this
research, the study of EEG signals is oriented towards the analysis and prediction of epileptic
seizure activity in the brain. It is important to mention the fact that there are other ways to
analyze and predict epileptic seizures; however, the objective of this thesis is to implement a
digital signal processing algorithm to predict the initial states of a seizure before it happens. To
achieve the intended goal, bio-instrumentation hardware such as the EMOTIV Epoc+ and Insight
are used to capture the analog EEG signals and software such as LabVIEW and MATLAB to process
the EEG signals.
According to the Epilepsy foundation, epilepsy is one of the most common neurological
problems. Around 2.2 million Americans report to have some sort of epilepsy but higher number
of people report to have active epilepsy and even higher numbers are reported when people
have been asked if they ever had epilepsy at some point in their lives [1].
1
1.2 Electroencephalography (EEG)
The discovery of the EEG has provided an avenue on the study of the human brain and its
interactions with the rest of the body. Since then, scientists and researchers are able to provide
a meaning for such a random non-periodic signal as the EEG. The range of amplitudes in EEG’s is
normally from 10 mV to 150 mV when recorded from electrodes from the scalp [1]. EEG signals
also contain frequency components that range from 0.5 to 50 Hertz. However, the most
important EEG frequency patterns are categorized in the following bands:
Delta band. Typically, from 0.1 to 3.5 Hz
Theta band. 4.5 Hz to 7.5 Hz
Alpha band. 8 to 13 Hz
Beta band. Typically, from 14 to 30 Hz
Figure 1: EEG waves by frequency [1]
In the past few years, the EEG has become popular among scientists and researchers who
study the control of electronics and mechanical devices using mental commands as well as those
2
who study epileptic seizure phenomena. However, it is necessary to have the right
instrumentation equipment to acquire an EEG signal regardless of the application.
Figure 2: EMOTIV Epoc+ and Insight EEG headsets. Courtesy of EMOTIV Brainware®
A seizure happens when a person’s brain is overloaded by abnormal amount of electrical
and chemical activity. Neurologists describe seizures as “storm in the brain” [2]. In the moment
a seizure happens, there are changes happening in how a person thinks, feels, or moves.
Depending on the types of seizure, these changes can cause thing like:
Loss of consciousness
Convulsions
Confusion
Brief periods of staring
A sudden feeling of fear or panic
Uncontrolled shaking of an arm or leg.
1.3 Epilepsy and Seizure Basics
3
There are over 20 types of seizures and the type may depend on where the activity
happens and how much the brain is involved. All types of seizures fall into two broad categories.
Generalized Seizures: Occur when the abnormal activity happens in both sides of the
brain at the same time.
Partial or Focal Seizures: Typically happen in one specific part of the brain.
1.3.1 Common Generalized Seizures [1]
Generalized Tonic-Clonic Seizures are the most common and well known type of seizure
for children. Tonic seizures begin with stiffening of the limbs and loss of consciousness.
Breathing may decrease or cease altogether. The clonic stage causes jerking that may
last several minutes.
Absence Seizures are characterized by a blank stare, rolling of the eyes, and chewing
movements. There is no confusion afterwards.
Atonic Seizures causes a loss of normal muscle tone. The affected person may fall or
drop down his or her head involuntarily.
Myoclonic Seizures cause sudden muscle jerks.
1.3.2 Common Partial or Focal Seizures [1]
Simple Partial (or focal) Seizures: this type of seizure may take many forms. Usually affect
movement on one part of the body, or it may affect a sensation. Typically, the affected
person stays awake and aware unless the seizures turns into a generalized seizure.
Complex Partial (or focal) Seizures: this type of seizure affects consciousness. The
affected person may start with a blank stare and then display strange, repetitious
4
behaviors such as blinks, twitches, mouth movements, removing clothes, or walking in a
circle without retaining memory of what happened during the seizure.
1.3.3 EEG Activity Terminology
Non-Ictal: A physiological state which is characterized by normal electrical activity in
neuronal tissue [3].
Ictal: A physiological state which is characterized by periods of high-frequency high
amplitude electrical activity in neuronal tissue [3].
Pre-Ictal: Electrical activity occurring before a seizure or a stroke.
1.4 Artifacts
EEG measurements techniques suffer from signal distortions that can cause higher
amplitudes and different shapes in the signal. These are called artifacts. Most of these artifacts
are unwanted physiological signals caused by the patient and may cause a major
misinterpretation of an EEG signal [4]. Other forms of artifacts are the power line loss and a
decrease in electrode impedance. Some of the most common artifacts found in EEG are the
following:
Patient related artifacts:
Electromyography (EMG)
Electrocardiography (ECG)
Eye and other minor body movements
5
Technical artifacts
50/60 Hertz noise due to AC line losses
Broken electrodes or contacts
Low Battery
Loss in data transmission
1.5 Conclusion
In this research, I explore the use of several EEG recordings from the Epilepsy Foundation
to implement and test the signal processing code written in LabVIEW. Also, implementation of
filtering and spectral analysis techniques will be briefly discussed. However, the main techniques
being used for this algorithm involve statistical calculations such as the mean and standard
deviation of a transformed EEG signal.
This thesis research project was developed in order to facilitate the use of a technology
that a layperson would be able to access using a home computer and an EEG headset device. The
goal of this research is to develop a prediction algorithm that would interface to LabVIEW source
code to extract prominent features of interest from EEG waveforms. Once that is accomplished,
this research will serve as a feasibility study on an early warning system for epileptic seizure
activity.
6
CHAPTER 2
METHODS
2.1 EEG Acquisition Device
An EMOTIV Epoc+ EEG headset was used to record data from subjects. Both, male and
female subjects were chosen on a voluntary basis. Each EEG recording lasted about five to ten
minutes while the subjects operated a computer or remained still. The sampling frequency of
the EMOTIV Epoc+ was set to capture 256 samples per second and a 60 hertz digital embedded
filter to keep unwanted noises caused by electric lines. The EMOTIV headset has fourteen
different channels that monitor the EEG from different regions of the brain and a ground node
at each side of the device [5]. A total of 16 probes are to be placed on the individual’s scalp.
In accordance with the international 10-20 system of electrodes for the EEG, the
EMOTIV Epoc+ headset contains the following channels:
Frontal: AF3, AF4, F3, F4, F7, and F8
Fronto-Central: FC5 and FC6
Occipital: O1 and O2
Parietal: P7 and P8
Temporal Sites: T7 and T8
References: CMS/DRL noise cancellation configuration P3 and P4 locations
Figure 3 graphically depicts the approximate locations where the EMOTIV Epoc+
headset places their electrodes.
7
Figure 3: EPOC headset Channel Position Map. Courtesy of EMOTIV Brainwear®.
2.1.1 Other EMOTIV Epoc+ Specifications
Connectivity
Wireless: Bluetooth® Smart
Proprietary wireless: 2.4GHz band
Power
Battery: Internal Lithium Polymer battery 480mAh
Battery life: up to 12 hours using proprietary wireless, up to 6 hours using Bluetooth®
Smart
2.1.2 EMOTIV Pure EEG Raw EEG Software
All the EEG recordings are captured and visualized using the EMOTIV Xavier composer,
which contains the root file to access the EEG raw data. The EMOTIV Xavier Composer-Raw EEG
Software is unique because it provides the ability to see which electrodes make good contact
with the human scalp. If there is not a good contact, as seen in Figure 4, a red indicator will turn
on along with the position of the electrode. However, if there is good contact, the indicators will
8
turn green. This feature proves itself useful for electrode-scalp contact when the EMOTIV Epoc+
is used with an external signal processing software. The EMOTIV Xavier Composer-Raw EEG
Software includes more capabilities such as data recording and a full view of the different signals
being recorded.
Figure 4: EMOTIV Xavier Composer-Raw EEG Software.
In addition to visualizing and recording EEG signals, EMOTIV Xavier Composer-Raw EEG
Software is able to display the fast Fourier transform of the current signals. Because this
software does not provide the capability of performing other signal processing or prediction
techniques, a different recording and signal processing approach had to be investigated. As a
solution, MathWorks Simulink offers an alternative user interface that communicates with the
EMOTIV Epoc+ headset.
2.2 Software
2.2.1 Simulink
MathWorks’ Simulink package included in MATLAB was explored to create a graphic
interface capable of allowing the user to display raw EEG signals and implement complex digital
9
signal processing. Despite the functionalities Simulink possesses, it contained very few ways to
track and display immediate results. In addition, Simulink ceased to be the signal processing
resource for this thesis research project due to the lack of documentation.
2.2.2 MATLAB
Matrix Laboratory (MATLAB) is an essential tool to make basic and complex calculations
allowing the user to process data much faster. For the purposes of this thesis, MATLAB serves as
a mean to read pre-recorded data from either the EMOTIV Epoc+/Insight or any other devices
that capture EEG signals. In some cases, when the data has been pre-recorded by a different EEG
device with different sampling frequency (usually higher than 256 samples per second), MATLAB
can help to down sample the EEG signal without corrupting data from the pre-recorded signal.
Most of the time, a pre-recorded EEG data set was found; however, the sampling
frequency would be 20 times higher than the sampling frequency of an EMOTIV device. The
reason why down sampling needs to be done to any non-EMOTIV recorded EEG data set is
because it is necessary to test the epileptic prediction algorithm as if it was taking data from an
EEG EMOTIV device. Fortunately, MATLAB facilitates this down sampling process by a technique
that could be termed data stitching or in more technical terms data concatenation.
When MATLAB reads a pre-recorded EEG data set, it reads it as a CSV file; however, these
CSV files have a limit on how much data they can store at a time. Chunks of data are
independently stored in a separate CSV files to avoid this problem. The EEG data is read by
MATLAB to be down sampled and stored in a new file with less. This process is applied to the
majority of the signal and in the end, it is possible to save a separate CSV file with fewer data.
10
Once again, all this CSV files are read in MATLAB and then concatenated with the other chunks
of the EEG signal into one EEG signal CSV file.
MATLAB also provides a digital filter design implementation tool for signal processing
purposes. Some of the pre-recorded EEG signals may or may not include unwanted signals that
interfere with the integrity an EEG recording. Most of this unwanted signals are caused by a 60
Hertz signal present in electric lines. Depending in the geographic location, a 50 Hertz signal may
be present as well.
2.2.3 LabVIEW
This software tool has similar capabilities as Simulink; however, LabVIEW provides more
ways to provide a better user interface and an easy to understand programming flow process. In
addition, LabVIEW supports parallel processing that enables more processing power to a given
code. In addition, National Instruments has developed a Virtual Instrument (VI) that allows
EMOTIV devices and LabVIEW communicate and transfer EEG data via a Bluetooth connection.
There are other functionalities that National Instruments added to their EMOTIV LabVIEW VI
package such as digital filtering and Fast Fourier transform capabilities. Once the communication
between the EMOTIV Epoc+ headset and LabVIEW is established, signal processing techniques
can be implemented and executed.
The hardest part of real time digital signal processing is the ability to keep track of data
stored in memory and data that has been processed by a given LabVIEW algorithm. To solve this
issue, all the EEG data obtained is being saved to a master Comma Separated Vector (CSV) File
and to a CSV dummy copy that tracks most recent data. This dummy copy only contains the last
11
and most recent 1280 samples at a given time, that is about 5 seconds of recorded data assuming
a sampling frequency of 256 samples per second.
Then the LabVIEW VI is set to extract 512 samples out of the 1280 samples previously
recorded and undergo through a digital processing algorithm. The extracted samples are erased
from the dummy copy leaving 768 samples. At the same time, the EMOTIV Epoc+ headset is still
feeding LabVIEW with new data to both master CSV file and the dummy copy. Once the dummy
copy reaches 1280 samples, LabVIEW will process another set of 512 samples in the order the
data was received while the master copy is saving all the data from the start. Thus, this becomes
an iterative process until the recoding comes to an end. More detailed explanations on how the
LabVIEW code is executed will be discussed in a later chapter.
2.3 Mathematical Background
To get a better grasp of the mathematical procedures used in thesis research study, it is
necessary to define some concepts ranging from general engineering mathematical operations
to statistical analysis.
2.3.1 The Fourier Transform
This mathematical procedure breaks down a complex signal or a waveform into its sine
and cosine and frequency domain representation [6] . In other words, the Fourier transform
converts a time signal into its frequency domain signal. In addition, an inverse Fourier transform
can be obtained from this process. The mathematical representation for the Fourier transform
and Inverse Fourier transform is by equation 1 and 2 respectively.
12
𝐹(𝜔) = ∫ 𝑓(𝑡)𝑒−𝑖𝜔𝑡𝑑𝑡
∞
−∞
(1)
𝑓(𝑡) =1
2𝜋∫ 𝐹(𝜔)𝑒𝑖𝜔𝑡𝑑𝜔
∞
−∞
(2)
where 𝑓(𝑡) represents the signal being analyzed and 𝜔 represents the frequency component.
2.3.2 The Discrete Fourier Transform (DFT)
Any continuous time domain signal x(t) can be represented in a set of “n” samples forming
a discrete time signal x(n) to be able to be processed by a digital system. This process is called
discretization and often performed by a sampler. In mathematical computations, it is possible to
obtain the Fourier transform of a discrete time signal by applying a similar process as the Fourier
Transform in continuous time domain. An inverse transform also holds true for this process. The
mathematical representation on how to obtain the DFT and IDFT given by equation 3 and 4.
𝐷𝐹𝑇 𝑋(𝑘) = ∑ 𝑥(𝑛)𝑒−𝑖2𝜋𝑘𝑛
𝑁
𝑁−1
𝑛=0
𝑘 = 0,1,2,3,… ,𝑁 − 1
(3)
𝐼𝐷𝐹𝑇 𝑥(𝑛) =1
𝑁∑ 𝑋(𝑘)𝑒
𝑖2𝜋𝑘𝑛𝑁
𝑁−1
𝑛=0
𝑛 = 0,1,2,3,… ,𝑁 − 1
(4)
2.3.3 The Fast Fourier Transform (FFT)
The FFT mathematical computation has the same results like the DFT; however, the FFT
reduces the computation complexity by obtaining faster results than the DFT [7]. In the same
manner, an inverse FFT can be performed to return an N point discrete time domain signal x(n).
13
2.3.4 Fourier Transform Digital Implementation
In this theses, all the Fourier transforms were obtained by using LabVIEW’s signal
processing package. This transform was uniquely used to capture the frequency components of
an EEG signal as it was recorded by the EMOTIV Epoc+ headset. In a later topic, a variation of the
FFT will be used to obtain the instantaneous frequency components of the empiric mode
decomposition (EMD) results. These instantaneous frequencies will determine the amount of
modes that fall in the 0-60 Hertz range.
2.3.5 Hilbert Huang Transform (HHT)
The use of the HHT is purely an empirical approach analysis that combines the empirical
mode decomposition and the Hilbert spectral analysis. This transform is designed to analyze
non-linear and non-stationary signal such as the EEG as it is in the case for this this research
project. Interestingly enough, the HHT transform provides much better results than the
traditional and well known time-frequency analysis techniques. Furthermore, the HHT has been
proven to reveal physical meanings in many of the data examined [8].
2.3.6 Empirical Mode Decomposition
The empirical mode decomposition (EMD) allows the extraction of important data in the
nonlinear and nonstationary signal in an intuitive and adaptive manner. As a result, the
representation of this empirical method is called intrinsic mode function (IMF). These IMFs
represent an oscillatory mode with a variable amplitude and instantaneous frequency as
functions of time [9]. An IMF should satisfy the following conditions:
14
a. In the whole data set, the number of extreme points and the number of zero crossings
must either equal or differ at most by one.
b. At every point, the mean value of the envelopes defined by local maxima and local
minima is zero.
In order to decompose any nonstationary and nonlinear function into IMFs one must do
the following sifting process:
1. Get the local maxima and minima of the recorded signal 𝑥(𝑡).
2. Get the upper envelope (𝐸𝑚𝑎𝑥) by connecting all the local maximums of the recorded
signal using a cubic spline function. The Cubic spline function can be found in LabVIEW.
3. Repeat process 2 for the local minimums and produce the lower envelop (𝐸𝑚𝑖𝑛).
4. Calculate the mean value at every point of both upper and lower envelopes.
𝐸𝑚𝑒𝑎𝑛 =(𝐸𝑚𝑎𝑥 + 𝐸𝑚𝑖𝑛)
2(5)
5. Let ℎ(𝑡) = 𝑥(𝑡) − 𝐸𝑚𝑒𝑎𝑛. If ℎ(𝑡) satisfies the IMF condition, then go to step 6
else, repeat process 1-5
6. If r(t) is a monotonic function, end the sifting process, else x(t)=r(t) and go back to step
1.
15
Figure 5: EMD Data, Mean, Upper and Lower Envelope
EMD works like an adaptive filter by shifting out the fastest changing component of a
composite signal first. The cutoff frequency of the filter is adaptive and data driven. IMFs are
generated in order of decreasing frequency. For example, IMF1 is the one-associated with the
locally highest frequency while the residue contains the lowest frequency. The main features of
the ictal EEG are closely related to the first few IMFs, and IMF1-IMF3 of each EEG segment are
used to extract the EEG features. [8]. Just like the sifting process describes, it is possible to see
that EMD is a very systematic, intuitive, and provides useful information about a given non-
stationary signal. For other types of studies, the EMD provides meaningful information that
reveals events in specific data.
It is essential to mention that this process has statistical methods to provide the
information we are looking for. The most important ones are mean (µ) and standard deviation
(𝜎) calculated using data from the IMFs. In the end, these two will serve to extract features from
the recorded EEG signal.
16
Figure 6: EEG IMF1 though IMF5 example
2.3.7 The Hilbert Transform
Once the IMFs are obtained through the sifting process, one will have no difficulty in
applying the Hilbert Transform to each IMF component. The Hilbert transform is useful in
calculating instantaneous attributes of a time series, especially the amplitude and frequency [10].
The instantaneous amplitude is the amplitude of the complex Hilbert transform; the
instantaneous frequency is the time rate of change of the instantaneous phase angle. For a pure
sinusoid, the instantaneous amplitude and frequency are constant. The instantaneous phase,
however, is a saw-tooth, reflecting how the local phase angle varies linearly over a single cycle.
For mixtures of sinusoids, the attributes are short term, or local, averages spanning no more than
two or three points [11].
2.3.8 Instantaneous Frequency
Typically, non-stationary signals do not decompose themselves into sinusoidal
components. For our case, the idea of frequency losses its effectiveness; however, it is necessary
to take into account for the time varying nature of the process and thus giving birth to the
17
instantaneous frequency [12]. Let 𝑓(𝑡) represent non-stationary signal as a function of time 𝑡.
The corresponding complex trace 𝑐(𝑡) is defined as:
𝑐(𝑡) = 𝑓(𝑡) + 𝑖 ℎ(𝑡),
(6)
where ℎ(𝑡) is the Hilbert transform of the real trace 𝑓(𝑡) [13]. One can also represent the
complex trace in terms of the envelope 𝐴(𝑡) and the instantaneous phase 𝜑(𝑡), as follows:
𝑐(𝑡) = 𝐴(𝑡)𝑐𝑖𝜑(𝑡)
(7)
By simple definition, instantaneous frequency is the time derivative of the instantaneous
phase[14]. Refer to the equation 8.
𝑤(𝑡) = 𝜑′(𝑡) = 𝐼𝑚 [𝑐′(𝑡)
𝑐(𝑡)] =
𝑓(𝑡)ℎ′(𝑡) − 𝑓′(𝑡)ℎ(𝑡)
𝑓2(𝑡) + ℎ2(𝑡)
(8)
2.3.9 Statistical Mean
In Mathematics and statistics, the mean is the summation of all the data divided by the
length number of data given in a specific set. That is,
𝜇 =(𝑥1 + 𝑥2 + 𝑥3 + 𝑥4 … .+𝑥𝑛)
𝑛(9)
or
18
𝜇 = 1
𝑁∑𝑥𝑖
𝑁
𝑖=1
(10)
where n is the length number of the data set and x is the index value at the 𝑖𝑡ℎ position
However, for our purpose; the mean equation looks slightly different, but the result is the
same. The mean formula is given by equation 11
𝜇 = 1
𝑙 ∑ |𝐼𝑀𝐹(𝑗)|
𝑙
𝑗=1
(11)
where 𝑙 is the length of the data in each IMF and 𝑗 is the individual value of the IMF in the 𝑗𝑡ℎ
position.
2.3.10 Standard Deviation
As it has been mentioned several times, an EEG signal is nonstationary and non-linear.
This simply implies that the signal has the characteristic of being random and it should be treated
differently when calculating the standard deviation [8]. The formula is given by equation 12.
𝜎 = √1
𝑁[(𝑥1 − 𝜇)2 + (𝑥2 − 𝜇)2 + ⋯+ (𝑥𝑁 − 𝜇)2],
(12)
where 𝜇 = 1
𝑁∑𝑥𝑖
𝑁
𝑖=1
(13)
Once again, the standard deviation formula looks slightly different, but in theory both
equations are the same. The standard deviation formula in terms of IMF is the following:
19
𝜎 = √1
𝑙∑ (𝐼𝑀𝐹(𝑗) − (
1
𝑙)∑ 𝐼𝑀𝐹(𝑗))2
𝑙
𝑗=1
𝑙
𝑗=1,
(14)
where 𝑙 is the length of the IMFs. In this research 𝑙 = 512, however; this number can vary or be
modified as needed. The number 512 was selected as it represents the number of samples being
processed at a time and is equivalent to two seconds of EEG data assuming a sampling frequency
of 256 samples per second.
Realistically speaking, not all EEG signal acquisition devices have the same sampling
frequency. The EMOTIV Epoc+ has a minimum sampling frequency of 128 samples per second
and a maximum sampling frequency of 256 samples per second. Consequently, most of the
numbers related to temporary stored data lengths will be multiples of 128. In a later chapter, it
is possible to see these numbers have an important role for this seizure prediction algorithm.
2.3.11 Feature Extraction
Feature extraction plays an important role in machine learning by providing details that
are not obvious to the naked eye. Common feature extraction techniques include histogram of
oriented gradients (HOG), speeded up robust features (SURF), local binary patterns (LBP), Haar
wavelets, and color histograms [15]. In machine learning and pattern recognition, features
contain relevant information that allows a prediction algorithm complete its task as efficient as
possible. However, for this thesis the most common feature extraction techniques will not be
explored or implemented.
In order to be able to predict whether an EEG signal has pre-ictal or non-ictal
characteristics, it is absolutely necessary to compute a few features to achieve our goal in this
20
thesis research. The calculated features are the Variation Coefficient, Fluctuation index, and
instantaneous frequency.
2.3.12 Variation Coefficient
Having already introduced the standard deviation and the mean in terms of IMF data, we
are able to obtain one of the features in the EEG signal based on these two statistical elements.
To calculate the Variation Coefficient, the IMFs’ standard deviation is divided by the mean of the
IMFs’ data set [9]. That is
𝐶𝑉 =𝜎2
𝜇2
(15)
where 𝜎 and 𝜇 represent the standard deviation and the mean respectively.
In simple terms, the variation coefficient tells us about the variations of a signal
amplitude. Also, this CV is applied to the first few IMFs of the EEG signal to explore more patterns
[16]. In chapter 4, the coefficient of variation results will be presented for completeness of this
thesis research project.
2.3.13 Fluctuation Index (FI)
This index serves as a feature and is used to measure the changes in the intensity of a
given signal [17]. The formula for Fi is given by equation 16
𝐹𝑖 = 1
𝑙 ∑|𝐼𝑀𝐹(𝑗 + 1) − 𝐼𝑀𝐹(𝑗)|
𝑙
𝑗=1
(16)
where 𝑙 represents the length of the IMF and 𝑗 is the index value of the IMF.
21
Based on the experiments performed in LabVIEW, the Fi in the pre-ictal stage is typically
a lot higher than the normal non-ictal stage. This pattern tends to exist in the subsequent IMF’s
calculated in LabVIEW. For completeness, the fluctuation index results obtained in the LabVIEW
simulations will be discussed in a later chapter. Graphics will be also presented to back up the
results.
22
2.3.14 Machine Learning Algorithms for Classification
Machine learning studies how to automatically learn to make accurate predictions based
on past observations [18]. Most classifications algorithms classify examples into set of categories;
however, the primary goal is to predict on test data.
Figure 7: Prediction Algorithm Flow Chart
Advantages of machine learning classifier:
Tend to be more accurate than human created rules.
Do not need a human expert.
The algorithm is flexible. It can apply to any kind of learning task.
Disadvantages:
Often require a lot of labeled data
Prone to errors- It is hard to get a perfect prediction accuracy
Labeled Training
Examples
Machine Learning Algorithm
Supervised/Unsupervised
Classification Rules
New Incoming
Examples
Predicted Classification
23
2.3.15 Machine Learning Algorithms
Machine learning algorithms find statistical regularities or patterns in the data. The most
important thing is that the algorithms allow a computer to learn; however, these machine
learning algorithms barely resemble human approaches to learn a task [19]. Some algorithm
types include:
Supervised Learning--- the main goal for this algorithm is to classify a problem by mapping
inputs to desired outputs. The algorithm approximates the behavior of a data set based
on a labeled training data set.
Unsupervised Learning --- very similar to supervised learning; however, a labeled training
data set is not provided for classification.
Semi-supervised Learning---this type of learning algorithm is a combination of the
supervised and unsupervised learning algorithms.
Reinforcement Learning--- this type of algorithms learns as it goes. It learns from the
environment is exposed and gets feedback to guide the learning algorithm.
2.3.16 Supervised Learning
Supervised learning algorithms uses a known training data set in order to classify or make
predictions. This training data set contains the input and output values that allow the algorithm
to build a model to make predictions given a new dataset [20]. Also, the machine learning
algorithm has to be validated by a test data set in order to verify the prediction power of the
algorithm itself.
24
Most common classification methods are the following:
Support vector machines (SVM)
Neural networks
Decision trees
Discriminant analysis
Nearest neighbors
For this thesis research project, I have chosen to use the supervised learning algorithms
route. The classification method used in the LabVIEW code is the support vector machine (SVM)
which has proven to be more effective than other supervised learning algorithms. Other learning
algorithms will be tested out; however, the emphasis is on the SVM.
2.3.17 Support Vector Machine
In essence, a SVM is a mathematical entity. An algorithm or recipe for maximizing a
particular mathematical function with respect to a given collection of data [21]. The SVM is a very
common biomedical application to classify different objects and some of this objects can vary
from DNA sequences to EEG readings. Because the SVM has demonstrated great power in the
Biomedical Engineering field, this classification method will be used here.
Types of SVM:
Linear classification SVM
Nonlinear classification SVM
25
2.3.18 Linear Classification SVM
A linear classifier separates a set of objects into their respective groups using imaginary
hyperplane lines [26]. These separating hyperplane lines are obtained through a training data set
that is considered to be linearly separable and the closest data points to the imaginary
hyperplanes are called support vectors. These hyperplanes are defined by the following
equations:
�⃗⃗� ∙ 𝑥 + 𝑏 = 1
(17)
and
�⃗⃗� ∙ 𝑥 + 𝑏 = −1
(18)
where �⃗⃗� is the normal vector to the hyper-plane [22].
However, the goal is to obtain a margin line that lies half way between both hyperplanes.
The margin line is obtained by calculating the maximum distance between both imaginary
hyperplanes. The maximum distance is calculated by the following formula:
𝑚 =2
‖�⃗⃗� ‖(19)
To maximize the value of 𝑚 , it is necessary to minimize the value of ‖�⃗⃗� ‖ [22]. In addition, it is
important that new data does not fall in between both imaginary separating hyperplanes
otherwise, there will be a misclassification problem.
26
Figure 8:SVM Support Vectors and Separating Hyperplane
In order to use a typical SVM, it is necessary to have the following: first, a training data
set to build a model. Second, a testing data set to prove the model predicts with a certain level
of accuracy. Once these data sets are obtained, the SVM can be refined to achieve better
results. Despite the greatness of a linear SVM, it will not the most efficient classifier for this
thesis project.
2.3.19 Nonlinear Classification SVM
EEGs are typically very random data that in the long run have a defined pattern when
features are extracted. However, a few feature data points don’t follow an expected pattern all
the time. For instance, a pre-ictal EEG might contain two feature data points that follow a non-
ictal pattern. When these features are feed into a linear classifier SVM, it is possible to have a
misclassification problem simply because there cannot be a linear hyperplane as a decision
surface [23]. To solve this problem, a nonlinear SVM has to be implemented to obtain better
results. The Nonlinear SVM generates a nonlinear decision surface that can classify nonlinearly
separable data. In chapter 4, the reader can see similar nonlinear characteristics as in Figure 9.
27
Figure 9: Nonlinear SVM example Data set [24]
2.4 LabVIEW Code
Designing or implementing code with LabVIEW provides an easier way to execute
functions in a systematic and a circuit type of building mechanism. As it has been mentioned
before, LabVIEW is a great tool for digital signal processing and so is the graphical user interface.
Without the real time user interface LabVIEW provides to the programmer, the outputs obtained
in a code (VI) would almost be meaningless to an external user.
Although LabVIEW offers and has many graphical user interface advantages, the portions
of a code could end up being a mess if proper LabVIEW programming techniques are not kept in
mind. In some cases, an organized code is almost impossible due to the complexity of the code.
The reader should be advised that certain figures could be hard to follow because of the
complexity of the code. Furthermore, an explanation of how certain parts of the code will be
provided. Next, a list on how the LabVIEW code is organized and implemented will be presented
hoping the reader fully understands the thought process of creating this epileptic seizure analysis
and prediction code.
28
2.5 LabVIEW Code List
In order to create the code, the whole project is divided into three major different parts:
1. EMOTIV Epoc+ headset wireless connection to LabVIEW VI
2. Signal processing and Feature Extraction VI
3. Classifier SVM VI
2.5.1 EMOTIV Epoc+ to LabVIEW
The interfacing of the EMOTIV Epoc+ to LabVIEW VI is was accomplished using the
National instruments EMOTIV Toolkit. This VI allows the user to see most of the features provided
by the Xavier-Raw EEG while providing access to the raw EEG data being recorded. This toolkit
plots and saves data as a CSV file for immediate or later processing. The advantage of this VI is
that it allows initial communication link between the EMOTIV headsets and LabVIEW without
having to create a link communication VI from scratch.
Now, this VI is intended for simple applications such as EEG and data transmission
monitoring. All the EEG channels in the VI user interface are brought together into one window
displaying each channel with different colors to distinguish one channel from the other.
Displaying all channels in one window presented a few problems. First, there is no way to tell
which channel belonged to an electrode in the EMOTIV Epoc+ EEG. Second, the EEG raw data
needs to be accessed individually by channels to process the signal. In order for code to work,
the VI had to be modified and redesigned to meet the requirements needed for this research
thesis project.
29
2.5.2 LabVIEW EMOTIV Toolkit V2
Before describing the modifications made to this VI, the following steps need to be
performed, to make this VI function according to National Instruments instructions:
1. Install the EMOTIV SDK software.
2. Have LabVIEW 2013 or above installed.
3. Localize the edk.dll file, this will allow the Epoc+ headset communicate with LabVIEW.
4. Run the VI in order to test functionality.
Figure 10: EMOTIV LabVIEW Toolkit VI (original Version) [25]
After applying all these changes, the EMOTIV Epoc+ must be on and the VI should be running to
display live EEG recordings. Figure 10 is a great representation of how the VI looks like when
the Epoc+ and the VI are capturing live data from all channels.Figure 10: EMOTIV LabVIEW
Toolkit VI (original Version)
2.5.3 EMOTIV Toolkit VI Modifications
First, it is necessary to look into the VIs block diagram to see the how the code was
organized. At specific points in the code, several indicators were placed to see what was
30
happening when the code executed. Finally, the place where all the EEG channels signal come in
an array was found. To separate the EEG channel signals, an Index array function is added to
select the channels independently. An index array function was added 14 times to the original
signal coming from the LabVIEW EMOTIV interface sub VI EEG data.
Figure 11: Original Plot EEG sub VI
Figure 12: Modified Plot EEG VI
31
Figure 12 shows the modifications made to the original code. Once the channels were
located, it was necessary to subtract 4000 to the incoming signals. If this number is not taken
away, then the amplitude of the digital signal would have been too high to display in LabVIEW’s
graphing window. Notice from Figure 11 that an amount of 4000 is also subtracted to all the
channels to avoid this problem. After the subtraction, the individual digital EEG signal-can be
visualized by adding a waveform graph visualizer. As soon as the waveform graph was added to
the block diagram, the graphical user interface or front panel is updated.
The goal was to save the EEG data every ‘N’ amount of samples into a CSV file. LabVIEW
provides a function that reads the amount of elements coming into an array and outputs that
number. When this element number reached any multiple of 2048, the code would output a
whole number.
The MATLAB script would then receive this whole number and check if the modulo of that
number was equal to zero or 0.5. If this statement was true, then the output ‘n’ equaled to one.
Otherwise, ‘n’ equaled to zero. The ‘n’ output was then transferred to a Boolean operator that
checked against another 1. If the ‘n’ output equaled to one, then a Boolean 1 would come at the
output. If the ‘n’ output was a zero, a Boolean 0 came at the output. This Boolean output
controlled the operation of the Case structure VI. The case structure VI changed according to the
numeric input given at the selector and executed a piece of code included in it. See Figure 14 for
reference.
32
Figure 13: Front Panel EEG Signal Reader
Figure 14: Case Structure Example Help
Figure 15: Modified EEG Reader with Case Structure
33
If the numeric input at the case structure selector of was a Boolean ‘1’, then case 1
executed by saving the data. If the input a was a Boolean ‘0’, then case 0 executed by doing
nothing. As this process happens, a similar code is running in parallel saving EEG data every 1280
samples. This number represents 5 seconds of EEG data assuming a sampling frequency of 256
samples in a second (5*256=1280). The MATLAB script works in the same way as the one
described earlier. Notice in
Figure 15, how the case structure changes for this part of the code by executing a similar code as
the one described before.
Figure 16:Modified EEG Reader with Case Structure Part 1
34
In Figure 16 and Figure 17, we can see the case structure implementation executing a
different set of codes. Basically, there are two cases either case ‘one’ or case ‘two’. When case
‘one’ is executed, two things happen in parallel. First, the information is saved directly to a CSV
file and second, 512 samples of data are extracted from the buffer and saved to a separate CSV
file. If data is available at the time, the previous 512 samples are deleted so that only 512 samples
are stored in the CSV file. The EMD VI later reads this CSV file to execute the code as soon as the
EEG data is available.
Figure 17:Case Structure to Save 512 Recorded Samples
2.6 Hilbert Huang Transform and EMD
This section will serve as an explanation of the organization in the contents found in this
VI. In addition, the exact implementation will be briefly discussed as the sifting process has been
described in chapter 1.
The main idea of having this VI was to execute the EMD and Hilbert spectrum processes
to extract the characteristic features found in pre-ictal and non-ictal EEG data recordings. This VI
starts by reading the contents found in the CSV file saved by the previous Plot EEG VI. Then, EDM
35
algorithm is executed and IMF outputs are displayed in an array format and graphs. This VI allows
the user to control the amount of IMFs displayed in LabVIEW’s user interface front panel. For
example, when the user selects ‘2’ modes, two results will be displayed in the front panel. Each
mode contains 512 results that will be transferred to section below in Figure 18.
Figure 18: Hilbert Huang Transform VI
The Hilbert transform sub VI receives this IMF data to compute the instantaneous
frequency components of the EEG signal. For each IMF data set, there will be a set of frequency
components at the output of the Hilbert transform sub VI [9]. The frequency components will
decrease as the amount of IMFs increases.
2.6.1 Feature Extraction
Once the IMFs were obtained using the EMD VI, the EEG data was analyzed from a
statistical point of view using LabVIEW Probability and statistic toolkit. Within this toolkit,
mathematical calculations such as the means, variance, and standard deviation can be performed
without the user having to create code out of scratch.
36
Since the EMD algorithm by nature provides several IMF modes, it was necessary to
create a code that would extract features depending on the number of modes present at the
output of the EMD code. If the user selects four IMF modes, then the feature extraction code will
be executed independently once per mode and a total of 4 sets of features will be saved to
memory. As mentioned in chapter 1, the features calculated are the ‘Coefficient of variation’ and
the ‘Fluctuation index’ which requires knowledge of the mean, variance, and standard-deviation
of each IMF obtained in an EEG segment. Each set of features is independently saved to separate
CSV file that will later be accessed by the user and the machine learning algorithm. Figure 19 and
Figure 20 provides a graphical representation of the code and the new front panel.
Figure 19: Fluctuation Index and Coefficient of Variation Extraction [26]
37
Figure 20:Front Panel for EEG readings, IMFs, VC and FI
2.6.2 Support Vector Machine: Supervised Learning
A SVM is able to classify according to a training data set. This data set has been obtained
by the automatic feature extraction algorithm discussed before in this chapter. To obtain this
data set, it is necessary to extract features from continuous segments of non-ictal and pre-ictal
EEG signals and save them to a CSV file which the SVM code will be able to read as the code runs.
First, the known training data set (features) is loaded to the Machine Learning Techniques
(MLT) 3D plot VI, while at the same time another MLT SVM learn sub VI receives the same
information to learn about the features. The output of the MLT SVM learn goes to the input of
the MLT SVM eval sub VI. The MLT SVM eval also receives a test set (unknown set) that needs to
be classified according to training data set given at the beginning. Lastly, when the decision has
been made by the MLT SMV eval sub VI, a plot of the result is displayed with their corresponding
categories and color. The training data set and the unknown test data set are read and stored in
a CSV file for future use and reference. The LabVIEW code can be seen in Figure 21.
38
Figure 21 SVM LabVIEW code Block diagram
2.7 Conclusion
Digital signal processing requires correct hardware and software to achieve the desired
goal. For instance, MATLAB is the software tool with the ability to read and process database
information that other software would not be able to do all in one package. LabVIEW facilitates
the coding process through a great user interface and the resources for signal processing are
enormous. In addition, the EMOTIV Epoc+ headset is an outstanding hardware providing state of
the art instrumentation electronics specialized for EEG studies and research.
In this chapter, the basic functionality of the different VI codes is explained as they
relate to the basic theory in chapter 2. The incorporation of three VI’s makes the task of data
acquisition, signal processing, and classification a simpler and fast way to obtain the desired
outcome. Once again, this chapter does not intend to explain how every single part of the code
works. However, this chapter gives the reader a basic idea on how the code was written and
39
how the desired results were obtained. In the next chapter, the results will be discussed.
40
CHAPTER 3
RESULTS AND DISCUSSION
In this chapter, my intentions are to explain the results obtained by all these VI codes.
These codes will be tested by using a data set containing both non-ictal and pre-ictal signals
obtained in an online EEG data base. The basic outline of the results will be the following: first, a
visual example of the EEG EMOTIV Epoch+ headset readings followed by the extraction of training
features using non-ictal and pre-ictal EEG signals. Second, the analysis on how a non-ictal and a
pre-ictal EEG signal differ from each other. Finally, a representation on how the support vector
machine performs data classification using a test data sets.
3.1 EEG Signal LabVIEW Reader
So far, the only way to read and process EEG signals is to transfer the data to a visualizer
interface. LabVIEW allows the EEG signals to be displayed without using an external software
provided by the creator of the EMOTIV headsets. In a previous section, the explanation of how
all the EGG signals are separated is provided. Figure 22 represents the result of the different
channels being displayed on separate windows.
Figure 22: EEG Signal LabVIEW Reader Front Panel
41
3.2 Non-Ictal and Pre-Ictal EEG Signal Analysis
This section will provide an analysis based on the results obtained by visually inspecting
non-ictal and pre-ictal signals. In addition, feature extraction results will be discussed along with
its tendencies and how the support vector machine processed the data given at its input.
3.3 EEG Signal Visual Inspection
In this thesis project an online data set (Epilepsy Foundation) that contains non-ictal and
pre-ictal EEG signal readings is used to test different parts of the LabVIEW code. Because this
data set has not been obtained using any EMOTIV devices, it is important to plot samples of non-
ictal and pre-ictal signals to verify that data set used in this thesis project had proper and useful
EEG readings. With the help of MATLAB, I have plotted about 10 minutes of EEG data to inspect
this EEG signals. Figure 23 and Figure 26 contain a visual example of the EEG data found in the
both non-ictal and pre-ictal segments EEG database.
Figure 23: Ten Minute Non-Ictal EEG Reading
42
Figure 24: Ten Minute Non-Ictal EEG Reading Continued
Figure 25:Ten Minute Non-Ictal EEG Reading Final
The EEG readings from Figure 23, Figure 24, and Figure 25 allowed me to see that this set
of segments contains good EEG readings; however, I was able to find segments of data that did
not contain any meaningful information to process. Let us look at the first few seconds of data in
Figure 26 to illustrate my point here. It is possible to see that the EEG signal does not exhibit EEG
patterns like those found in the Figure 23, 23, and 24. It is not until a few seconds later in the pre-
ictal set, that we are able to see some real EEG readings that contain useful information. Again,
this serves as an example of how useful the EEG recordings are in this online data set. Once good
segments of EEG signal readings were found, the information was exported to LabVIEW to extract
the training features for the Support Vector Machine.
43
Figure 26:Ten Minute Pre-Ictal EEG Reading
Figure 27:Ten Minute Pre-Ictal EEG Reading Continued
Figure 28:Ten Minute Pre-Ictal EEG Reading Final Section
44
3.4 EMD and Features
When good non-ictal and pre-ictal EEG readings were found, these signals were
independently loaded to the EMD VI to obtain the IMFs specified by the user. Figure 29 is an
illustration of the EMD at work when the IMF modes are set to five. Also notice how the EEG
signal and the EMD residue is being plotted as the algorithm reads 512 samples of data at a time.
Figure 29: EMD Front Panel with EEG, IMFs, and Features Display
3.5 SVM Training Data Set Analysis
The most important task is found in this section because without these information, there
is no way to predict whether an EEG signal falls in a non-ictal or pre-ictal class. To find a useful
training data set, known pre-ictal and non-ictal sets have been loaded to the feature extraction
VI (EMD). The following table represents a portion of the features extracted using the EMD VI
LabVIEW code.
45
Non-Ictal VC Non-ictal FI Pre-ictal VC Pre-ictal FI
2.719 4.473 1.705 5.536
2.144 3.874 1.832 5.187
2.929 4.02 2.453 6.331
2.628 3.953 1.587 5.949
2.757 5.772 1.951 5.36
2.282 4.358 2.118 6.716
3.94 4.988 2.466 6.703
2.209 5.558 2.101 5.663
2.017 4.171 2.866 7.364
2.29 4.851 3.499 7.303
1.79 4.732 1.773 5.559
2.046 4.386 1.701 5.507
5.969 5.071 1.825 6.725
3.283 4.651 1.522 5.29
2.977 4.469 2.81 5.965
3.567 5.341 2.048 5.63
2.283 4.144 1.616 5.472
2.714 4.591 1.833 5.989
46
Table 3.1: Features extracted using the EMD VI LabVIEW code.
This table does not represent all the values that resulted from the EMD code feature
extractor. A total of 584 set of features were extracted; however, table can be lengthy for
demonstration purposes. Figure 30 and Figure 31 represent the difference in amplitudes of a
non-ictal and pre-ictal Variation coefficient and Fluctuation index.
Figure 30:Variation Coefficient: Non- Ictal and Pre-Ictal Values
From the graph above, it is possible to see that the average Variation Coefficient in the
non-ictal EEG signal can be higher than the Pre-ictal EEG signal. However, the amplitude
difference is not as much as expected. In some cases, the Pre-ictal signal is higher in few
instances.
47
Figure 31:Fluctuation Index: Non-Ictal and Pre-Ictal Values
The fluctuation index for both of these type of EEG signals is significantly different in
amplitude. The non-ictal EEG shows much more fluctuations than the Pre-ictal signal. This causes
a relief, since it is possible to see a feature that shows a difference in both types of EEG signals.
A more interesting result is seen in Figure 31, when the all these data is plotted seeing the
“Variation Coefficient Vs Fluctuation Index” graph using LabVIEW.
In a study found in [9], similar results were obtained for the classification of inter-ictal and
ictal EEGs. Refer to Figure 32. The coefficients of variation in the ictal EEG are lower than those
found in inter-ictal EEG segments.
Figure 32 Variation Coefficients for Ictal and Inter-ictal EEG segments
48
In Figure 33 , the fluctuation index in the inter-ictal EEG segments appear to be lower than
those in ictal EEG segments.
Figure 33 Fluctuation Index for Ictal and Inter-Ictal EEG segments [9].
Looking at the results in Figure 32 and Figure 33, it is possible to see that the results
obtained in this thesis project are not far from other ones.
Figure 34:Variation Coefficient Vs Fluctuation Index Plot
The green dots represent non-ictal features and the red dots represent the pre-ictal
features. Outstanding results can be seen in Figure 34, as the non-ictal features are clustered
together in the same area and the pre-ictal features are clustered close to each other in a
different area. There are some non-ictal and pre-ictal features that fall within a cluster of data
49
that does not belong to their corresponding group. To fix this problem, non-ictal features and
pre-ictal features values are subtracted to see how much they differ from each other; therefore,
only keeping those features that contain the most difference in value. As a result, the refinement
of the training data set was achieved. The following table contains the difference non-ictal and
pre-ictal features with highlighted values were deleted.
Non-Ictal VC Pre-ictal VC Difference Non-ictal FI Pre-ictal FI Difference
2.719 1.705 1.014 4.473 5.536 1.063
2.144 1.832 0.312 3.874 5.187 1.313
2.929 2.453 0.476 4.02 6.331 2.311
2.628 1.587 1.041 3.953 5.949 1.996
2.757 1.951 0.806 5.772 5.36 -0.412
2.282 2.118 0.164 4.358 6.716 2.358
3.94 2.466 1.474 4.988 6.703 1.715
2.209 2.101 0.108 5.558 5.663 0.105
2.017 2.866 -0.849 4.171 7.364 3.193
2.29 3.499 -1.209 4.851 7.303 2.452
1.79 1.773 0.017 4.732 5.559 0.827
2.046 1.701 0.345 4.386 5.507 1.121
5.969 1.825 4.144 5.071 6.725 1.654
3.283 1.522 1.761 4.651 5.29 0.639
2.977 2.81 0.167 4.469 5.965 1.496
3.567 2.048 1.519 5.341 5.63 0.289
2.283 1.616 0.667 4.144 5.472 1.328
2.714 1.833 0.881 4.591 5.989 1.398
1.859 2.428 -0.569 4.016 5.622 1.606
2.472 1.658 0.814 4.442 6.872 2.43
2.146 2.787 -0.641 3.731 7.56 3.829
2.036 2.373 -0.337 4.16 6.688 2.528
2.219 1.578 0.641 5.077 6.216 1.139
2.469 3.243 -0.774 4.25 6.345 2.095
2.418 4.04 -1.622 4.191 6.514 2.323
4.257 1.639 2.618 5.037 6.037 1
2.18 2.498 -0.318 4.051 5.453 1.402
2.447 1.68 0.767 4.352 5.334 0.982
50
Table 3.2: Differences between non-ictal and pre-ictal features.
Again for simplicity, Table 3. 2 is not completely displayed. As it can be seen in this table,
the highlighted features were deleted to get rid of some features that do not contain significant
information.
Figure 35:SVM Refined Training Data Plot
If Figure 34 and Figure 35 are compared against each other, it is possible to appreciate
how some of the non-ictal and pre-ictal features do not overlap on each other as much as before.
My hopes are that with this result, the SVM can predict with better accuracy than using the
previous training data set. Now, let us see the predicting results of the SVM using this new
training data set.
To test this training data set, the same training data containing only the non-ictal values
is used. The expected result is that the SVM will recognize the only the non-ictal values (green
dots) at the output graph. After running the SVM VI, the results were as expected. There are a
51
few non-ictal points that were misclassified as pre-ictal (red dots); however, the SVM classified
most of the data as non-ictal. Figure 36 contains the graphical representation of the results.
Figure 36: SVM Classifier Test 1
A similar test was done to test if the SVM classified the pre-ictal values correctly. To
achieve this, another test data set was created only containing pre-ictal values in it to verify the
accuracy of the SVM. After running the SVM VI, the results obtained were as expected. Once
again, some pre-ictal values were misclassified by the algorithm. Figure 37 contains the graphical
results of the SVM.
Figure 37:SVM Classifier Test 2
52
CHAPTER 4
COMMENTS, FUTURE WORK, AND CONCLUSION
4.1 The Next Generation
At the very beginning, the main idea for this project was to have a cellphone application
that would directly connect to the EMOTIV Epoc+ headset to perform signal processing and be
able to detect epileptic activity from there. As time passed, this idea seemed almost impossible
for me as I was not very acquainted with cellphone application development; however, the idea
of having a local epileptic detection system was still implementable in a different platform with
a different processing source which in this case is a normal computer. As we know, almost
anybody in the United States has a home computer and the idea of implementing an epileptic
detection system became more feasible and doable. Over time, the idea of having a local epileptic
detection system grew more and more as this technology could be used in hospitals and local
care centers.
Despite the greatness of having a local epileptic detection software, the issue comes
when we think about those young college students and active adults who struggle and fight with
epilepsy. Whatever the case is, a next generation of portable epileptic detection system will be
developed using our everyday cellphone and an aesthetical engineered headset.
4.2 Other Considerations
In most of the EEG’s collected for this thesis project, the test individual was instructed not
to talk or make any facial movement that would affect the integrity of the EEG signal. The reality
is that if we are to develop an epileptic detection system, it is necessary to consider facial
53
movements that change the raw EEG reading. In future works, if we want to develop a more
precise local or mobile epileptic detection systems then, it is imperative to consider EMG signals
and EOG signals. An EMG would detect facial movements and EOG would detect ocular
movements (a.k.a artefacts). These facial and ocular movements happen all the time and cannot
be ignored if we want to achieve a more precise epileptic detection system. The EMG and EOG
will have to be subtracted from the actual EEG; however, this implies that a more sophisticated
instrumentation hardware will need to be developed to capture all these signals at once and
make the appropriate subtraction when needed.
4.3 Future Work
In the following sections, I had written out some ideas, comments, and input from an
epileptic patient and a Cardiologist. My biggest hope is that I can take all this information and
make it a reality once I have achieved a more effective system.
4.3.1 A Doctor's Recommendation
In the fall of 2015, I had the chance to meet Dr. Alo, a cardiologist who practices in Denton,
Texas and a member of the University of North Texas Biomedical Engineering Board committee,
proposed a great idea based on previous experience. He commented that some
doctors/neurologists don’t necessarily want a system that predicts whether a patient could have
an epileptic seizure or a heart attack. A doctor/neurologist may want to see other characteristics
or features in the recorded signal to better analyze and diagnose a patient’s condition.
This implies that a separate study must be performed to obtain the results doctors really
want to see. To achieve this goal in the near future, an extensive set of interviews to doctors and
54
EEG specialists must be conducted to obtain a better understanding of their needs. Based on
their needs, then a new system can be implemented and who knows, maybe current epileptic
prediction algorithms are somewhat close to what doctors are wanting to have in hospitals.
4.3.2 A Patient’s Perspective
Early in the fall semester of 2016, a freshman student from biomedical engineering
expressed interest in the study of epilepsy. The student mentioned that she struggled with
epilepsy and she was more than welcome to answer any questions regarding her experience with
epilepsy. However, I want to note that this student volunteered and that neither me or the
biomedical engineering department has forced her to answer or provide other information for
this research.
For the purpose of this research and future research, I have asked her a set of questions
and she provided a response.
1. Have you felt any physical discomfort before a seizure? Describe what you have felt?
a. Roughly thirty seconds to a minute before my first seizure I felt something weird
that I now know is an aura. An aura is a sensation or a warning that happens right
before a seizure and they are different for everyone, of course. It was 2007 and I
was nine and my mom and I were shopping, I walked to her and I told her
“something is not right” and I went into Grand Mal seizure. Later I was able to
describe what I felt beforehand. I felt as if my entire body was shutting down, I
thought I could feel every system in my body wanting to stop. The next I remember
is waking up on the floor surrounded by strangers staring at me and crying while
55
mom told me what happened and that paramedics were on the way. I now
describe my aura as “feeling off”. May of 2015 I had another Grand Mal seizure.
I had a normal day at school and came home to take a short nap. This nap turned
into a four-hour nap that I almost would not wake up from. I finally got out of bed
to try to eat something but I could not hold a conversation or keep my eyes open
to eat. At this time, I should have realized that was my warning but I was in denial.
My mom made me follow her to her bedroom, the great mom and nurse inside
her knew something was wrong, as she showered I sat next to a cabinet in her
room so she could know if something happened. I was trying to hold a
conversation and trying to text a friend so I could keep myself distracted from the
feeling a possible seizure. The last thing I remember before that seizure was
setting my phone on the counter, my mom said I began to seize then I hit my
forehead on the cabinet. The next thing I remember was crying on the floor while
the paramedics lifted me on a stretcher. I had a hard time making sentences,
remembering names, objects, and just generally my brain was not functioning
correctly for about two weeks because I had seizure brain activity for about three-
five hours. My next Grand Mal seizure was December 2015 on a road trip. I had
fell asleep against the window in the back seat, without anything covering the
window or my eyes, and while I was asleep we went down a road with trees (which
will make more sense in question 3). For this seizure I had no aura because I was
asleep and never woke up for it. I was told that we pulled over, and there was no
way to call for help in case the seizure lasted longer than four minutes, and my
56
mom and her husband pulled me into the ditch so she could safely administer my
Diastat valium syringe to stop the seizure. They said the seizing stopped but I was
still unresponsive when I threw up, a side effect from the valium apparently. Just
like before when I woke up I was disoriented, crying, and extra confused since the
last thing I knew was that I was asleep.
b. So basically my aura causes discomfort but I do not smell or see anything distinct
like some epileptics do. I begin to feel disorientated, dizzy, my fingers twitch, my
vision is not clear. There is no clear way to describe it honestly, I know when it is
about to happen but all I can describe it as is “I feel weird, I feel off”. So I am not
sure if that can be considered physical but it is certainly uncomfortable.
2. Can you describe the physical implications after a seizure? If any.
a. I have been lucky enough to not have broken any bones or have any major injuries
after seizures, especially considering my first seizure I was next to a wall of glass
cases. I had a bruised forehead had memory loss for two weeks after my second
Grand Mal seizure. I have had a few Absence seizures, also known as Petit Mal,
but the most painful Absence I had was while playing softball. I was a catcher and
I spaced out for a few seconds and got hit right between the eyes with a 70 mph
pitch (even though I had my catcher’s mask on it hurt horribly). After any kind of
seizure or “abnormal brain activity”, I am always extremely sore all over
afterwards for days and sometimes weeks; I feel as if I have been run over by a
train.
3. Based on your experience, what do you think triggers your epilepsy?
57
a. The triggers my neurologists always warn me about are lack of sleep, missed
medicine, and flashing/strobe lights. Sadly, they have all proven to be true. My
epilepsy was not passed down through generations but instead I had a seizure one
day that happened to be epileptic, therefore no one has a reason as two why I had
my first seizure. My second Grand Mal was from missed medicine. It was my fault
considering I had 14 days’ worth of medicine left in a 30-day bottle at the end of
the month. My third Grand Mal seizure was a combination of lack of sleep, missed
medicine, and flashing lights. I was not taking my medicine on time, we were on
a road trip so I wasn’t sleeping on a normal schedule, and I fell asleep against the
car window with the sunlight (strobe light style). Now on road trips I bring a mask
and I take my medicine at 7 a.m. and 7 p.m. to keep a steady 12-hour interval
between doses.
I, "Jane Smith" (pseudonym), willingly volunteered to answer these questions.
These questions were asked because we would like to know how the body responds
before and after a seizure. As a researcher, I would like to know if there are other parts of the
body that could provide useful information to detect an epileptic seizure other than the
conventional EEG readings.
4.3.3 Personal Comments
Based on Jane’s response, I found really interesting the “Aura” feeling she
experienced minutes before her seizure. My intuition tells me there could be some sort of
relationship between pre-ictal activity and this so called “Aura” sensation/warning. In Jane’s
58
December 2015 seizure incident, she was asleep while the seizure occurred. I believ, if there is
such a relationship between pre-ictal activity and the aura sensation and if she had been using
an EEG device connected to an epileptic seizure prediction system, this situation could have been
handled better or avoided.
On a different question, Jane said that lack of sleep and medication timing play an
important role when it comes to avoiding possible seizures. Current technology already tracks a
person’s sleep cycles, so the incorporation of this feature could be easily added to a future
system.
4.3.4 Emergency Response Protocol
The ultimate goal is to have mobile system that is able to predict an epileptic seizure. In
addition, the system should be able to keep a log of events in case an epileptic seizure happens.
Furthermore, the mobile system should have an “Emergency Response Protocol” feature. This
protocol should be activated once the pre-ictal activity is detected. The seizure emergency
protocol should automatically perform the following:
1. Contact a local or personal nurse. If applicable.
2. Call 911 for transport.
3. Notify a close relative or an emergency contact.
4. Notify a doctor/neurologist.
5. Administer emergency medications if available.
59
4.3.5 A Mobile Phone Application
Currently, the EMOTIV headset devices do not count with an official phone application
that is able to directly process EEG signals using digital signal processing techniques. Such
application must be able to do the same things that were accomplished using LabVIEW, including
the SVM classification algorithm. See Figure 38 for more details. As of now, a high school student
from Frisco, Texas has been working on this mobile application. However, his goal is to establish
a direct connection from the EMOTIV headset device to the mobile app. No signal processing
techniques has been applied to this app. In my opinion, the more people working on this
research, the faster we can reach the goal of having a mobile seizure prediction system.
Figure 38: Block Diagram Cellphone App Feature
4.3.6 Adaptation of an EMOTIV Headset
The reason why the EMOTIV headset needs to be modified is because not a lot of people
want to expose the fact they have an electronic device on their heads. One way to solve this
aesthetical problem is to adapt the EMOTIV device into a hat which will conceal most of the
electrodes found in current EMOTIV devices. Electronic hardware and antennas will have to be
60
placed outside the hat to avoid communication problems or exposure to bodily liquids such as
sweat. Also, proper isolation must be provided to avoid external liquids going into the electronics.
Many ideas like mine have been designed, but none of them are as efficient as the EMOTIV
headset devices. However, in the near future; I would like to take the challenge of designing a
device like the one I have described.
4.4 Conclusion
The pursuit of epilepsy detection can offer a lot relief to those who struggle with it. As
algorithms and techniques to detect these seizures arise every year, it is necessary to always keep
in mind that not everybody who has a form of epilepsy experiences the same effecs. Therefore;
more research has to be done to fully understand the mechanics of epileptic EEGs. In this thesis,
I have gained more understanding about epilepsies, seizures, and EEG signal processing;
however, there are more experiments that need to be conducted like live EEG signal recording
of an epileptic seizure. The implications and the risks of having an in house experiment like this
one are really high and should be done with the consent of the patient and an experienced
neurologist. Now, this type of experiment can be done; however, it could take months to find a
patient who is willing to collaborate and permission from the Internal Review Board at the
University of North Texas. On a different note, the epileptic seizure detection algorithm that I
have put together in this thesis project needs more modifications to perform better in different
conditions. Thus, I will continue my research in this really hot topic and eventually pass the
knowledge I have gained to upcoming researchers. I can only imagine on the day, people using a
system like this to prevent seizures that could have led to the death of a person.
61
The whole idea of this project was to implement a digital system that anyone at home can
have access from a computer. Also, my idea was to have a practical system in which anyone
interested in this topic could easily learn the mechanics of the seizure detection. This thesis
project allowed me to take several conceptual topics in signal processing and machine learning
to a practical realization of a system that uses both hardware and software. In addition, the idea
of having LabVIEW as a signal processing tool proved to be the most efficient. Not only facilitated
the creation of a graphic user interface of the system, but also makes it easier to teach new
incoming researchers how to use or change the algorithm. I believe, it is also important to
mention that a system like this should be expanded to mobile versions while taking the advantage
of cell phones processing power.
In conclusion, the use of a system like will change many people’s lives and hopefully
overcome the fear of an epileptic seizure attack. As I have mentioned in previous chapters, EEG’s
are naturally random signals; however, they do have features like the fluctuation Index and
variation coefficient that provide significant differences between a non-ictal EEG or a pre-ictal
EEG. Notice, I have left out instantaneous frequency as a feature for now. Needless to say,
frequency analysis should not be left out as feature for future experiments. The results obtained
from the SVM non-linear classifier yield good results when tested with non-ictal and pre-ictal EEG
signals in database. The amount of time to process two to four seconds of EEG data through the
whole code takes about 2 seconds. This almost instantaneous results could potentially warn a
patient with enough time to take precautionary actions. Finally, the decision factor/threshold on
the ratio of non-ictal and pre-ictal features in the same EEG reading to decide whether a person
62
could potentially be experiencing a pre-seizure activity needs to be determined by an
experienced neurologist.
63
REFERENCES
[1] C. Controls, "EEG – Electroencephaloraphy with LabVIEW and Mindwave Mobile," July
2015. [Online]. Available: http://cerescontrols.com/projects/eeg-electroencephalography-
with-labview-and-mindwave-mobile/.
[2] Epilepsy Foundation of America, Living with Epilepsy and Seizures, 2015.
[3] Neurolex, "Non-ictal," 2009 May 2009. [Online]. Available:
http://neurolex.org/wiki/Category:Non-ictal.
[4] M. Teplan, "FUNDAMENTALS OF EEG MEASUREMENT," 2002. [Online]. Available:
http://www.measurement.sk/2002/S2/Teplan.pdf. [Accessed August 2016].
[5] EMOTIV Inc., "Epoc+," 2016. [Online]. Available: https://www.EMOTIV.com/epoc/.
[6] Bevel, "Fourier Transform," [Online]. Available: http://www.thefouriertransform.com/.
[7] J. W. Cooley, "The Re-Discovery of the Fast Fourier Transform Algorithm," Mikrochimica
Acta, pp. 33-45, 13 October 1987.
[8] N. E. Huang, "INTRODUCTION TO THE HILBERT HUANG TRANSFORM AND ITS RELATED
MATHEMATICAL PROBLEMS," Interdisciplinary Mathematical Sciences , pp. 1-26, 2014.
[9] S. Li, W. Zhou, Q. Yuan, S. Geng and D. Cai, "Feature extraction and recognition of icta
lEEG using EMD and SVM," Computers in Biology and Medicine, pp. 807-816, 2010.
64
[10] F. R. Kschischang, 22 October 2006. [Online]. Available:
http://www.comm.utoronto.ca/frank/papers/hilbert.pdf.
[11] Mathworks, "Discrete-time analytic signal using Hilbert transform," 2016. [Online].
Available: http://www.mathworks.com/help/signal/ref/hilbert.html.
[12] S. Fomel, "Local seismic attributes," Geophysics, pp. 1-8, 02 March 2007.
[13] B. BOASHASH, "University of California," 4 April 1992. [Online]. Available:
https://www.math.ucdavis.edu/~saito/data/sonar/boashash1.pdf.
[14] Taner, M. T., F. Koehler and R. Sheriff, "Complex seismic trace analysis: Geophysics,"
Geophysics, pp. 1041-1063, 1979.
[15] MathWorks, "Feature extraction for compact representation of image data in computer
vision," 2016. [Online]. Available: http://www.mathworks.com/discovery/feature-
extraction.html.
[16] S. Ren and A. Fan, "K-means Clustering Algorithm Based On Coefficient Of Variation," 4th
International Congress on Image and Signal Processing, pp. 2076-2079, 2011.
[17] Mitha.M, S. S. S and M. V, "Automated Epileptic Seizure Detection using Relevant Features
in Support Vector Machines," International Conference on Control, Instrumentation,
Communication and Computational Technologies (ICCICCT), pp. 1000-1004, 2014.
65
[18] R. Schapire, "Machine Learning Algorithms for Classification Machine Learning Algorithms
for Classification," [Online]. Available:
http://www.cs.princeton.edu/~schapire/talks/picasso-minicourse.pdf.
[19] T. O. Ayodele, "Types of Machine Learning Algorithms," [Online]. Available:
citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.469.8077&rep=rep1&type=pdf.
[20] M. L. MathWorks, "Machine learning technique for building predictive models from
known input and response data," 2016. [Online]. Available:
https://www.mathworks.com/discovery/supervised-learning.html.
[21] W. S. Noble, "What is a support vector machine?," Nature Biotechnology, pp. 1565-1567,
2006.
[22] Wikipedia, "Support vector machine," 10 October 2016. [Online]. Available:
https://en.wikipedia.org/wiki/Support_vector_machine#Linear_SVM.
[23] K. Chin, "Nonlinear Support Vector Machine," 10 September 1998. [Online]. Available:
http://svr-www.eng.cam.ac.uk/~kkc21/thesis_main/node14.html.
[24] A. Sharma, "Support Vector Machine without tears," 7 August 2014. [Online]. Available:
http://www.slideshare.net/ankitksharma/svm-37753690.
[25] Cassani, "LabVIEW EMOTIV Toolkit V2," 3 December 2014. [Online]. Available:
https://decibel.ni.com/content/docs/DOC-40699.
66
[26] StatSoft, "Support Vector Machines Introductory Overview," 2016. [Online]. Available:
documentation.statsoft.com/STATISTICA. [Accessed 13 August 2016].
67