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Chapter 4

Analysis and simulation of EEG Brain Signal Data using MATLAB

4.1 INTRODUCTION

Electroencephalogram (EEG) remains a brain signal processing technique that

let gaining the appreciative of the difficult internal machines of the brain and irregular

brain waves ensures to be connected through articular brain disorders. The analysis of

brain waves shows a significant part in the diagnosis of dissimilar brain syndromes.

MATLAB delivers a cooperative graphical user interface (GUI) letting users to

openly and interactively route their high density EEG dataset then additional brain

signal information dissimilar methods like independent component analysis (ICA) and

time/frequency analysis (TFA). In addition to fixed averaging methods. The research

work resolve display dissimilar brain signals through associating, analysing then

simulating datasets which is before encumbered in the MATLAB software to practice

the EEG signals. The human brain is one of the greatest composite structures in the

creation. Currently many technologies are to record brain waves then

electroencephalography (EEG) remains one of them. This remains one of the brain

signals processing technique that permits attainment the thoughtful of the difficult

internal mechanisms of the brain and abnormal brain waves have shown to be

associated with particular brain disorders.

4.2 LITERATURE REVIEW

Creusere et al (2012), “Assessment of subjective brain wave form quality from

EEG brain replies via time space frequency analysis”, page 2704-2708. Theories give

details herein and research work is the problem of quantifying changes in the

perceived quality of signals by directly measuring the brain wave responses of human

subjects using EEG technique. Ideas taken on from this research work are that has

preferred an approach constructed on time space frequency analysis of EEG wave

form set for detecting different brain disorders.

Jutgla et al (2012)” Diagnosis of Alzheimer’s disease from EEG by means of

synchrony measures in optimized frequency bands”, page 4266-4267. Theories give

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details herein research work is the EEG is considered as a promising diagnostic tool

for analysing brain disorders symptoms because of its non-invasive safe and easy to

use properties. EEG has the potential to complement or replace some of the current

tradition diagnostic techniques. Ideas taken from this research work are EEG datasets

of the patients with different brain disorders symptoms have been collected to

diagnosis the seizures symptoms related to the patients.

Sosa et al (2011) reported in theories give details herein research work is the

operational procedures of EEGLAB and efficiency of EEG signal processing for

students and professionals to perform and analysis of the EEG signals. Its use as a

starting point for the comparison of different brain signal processing algorithms. Ideas

taken from this research work are Capabilities of EEGLAB for diagnosis purpose and

basic explanation of the working procedure of that tool for signal processing such as –

loading the dataset, plotting techniques to get the proper result, etc.

Bhattacharya et al (2011) theories give details herein research work Presented

the information about EEGLAB software for Brain-computer interface (BCI) is an

emerging technology which aims to convey people's intentions to the outside world

directly from their thoughts. Ideas taken from this research work are the Feature

learning of EEG to the classification among frequencies in tribunals and within

recording locations. Methods to allow users to remove data channels, artifacts by

accepting or rejecting visually.

Michalopolous et al (2011) reported that the Characterization of evoked and

induced activity in EEG and assessment of intertrail variability”, page 978-988.

Theories give details herein research work is the brain reply to an internal or external

experience is poised through the superposition of suggested and persuaded brain

activity which reproduces dissimilar brain mechanisms involved. Caminiti (2010)

reported that the identification of different brain activities through EEG assessment

procedure. Ideas taken from this research work are identifying brain activities for

diagnostic purposes and provide useful tools for brain computer interfaces through

insight on the activation of different brain channels

Ye Yuan (2010) theories give details herein research work; EEG dataset is

collected after analysing the entire length of the EEG recording the patient frequently

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for long time to detect traces of different human brain activities. Ideas taken from this

research work are change of the structure of different brain activities during seizures

is observed by the change of embedding dimension of EEG signals if the human brain

is considered as a nonlinear dynamic system.

Duque Grajales J.E., Múnera Perafán A., Trujillo Cano D., Urrego Higuita

D.A., Hernández Valdivieso A.M.(2009),” System for Processing and Simulation of

Brain Signals”, Page 340-345. Theories give details herein research work has

presented the methodology used to develop a system useful in the simulation of brain

signals. It has been described in detail the procedure in the modelling of EEG signals

and insight brain signals recorded during surgical procedures. Ideas taken from this

research work are processing and simulation of brain signals from different signal

processing models which allows going deep into the study of brain function during

sleeping and pathological situations and facilitated the assessment of the effect of

different drugs in different brain disorders.

4.3 BRAIN SIGNAL PROCESSING

Signal processing is the supporting technology for the generation, conversion,

also understanding of data. On dissimilar phases of period, human brain responds

contrarily. All these brain signals castoff for several purposes so that it is conceivable

to train the functionalities of brain suitably by creating, converting and interpreting

the collected signal. This progression is acknowledged as brain signal processing.

4.3.1 Brain Waves and EEG

The study of brain waves shows a significant part in the analysis of dissimilar

brain disorders. Brain is fabricated of billions of brain cells named neurons, which use

electricity to interconnect with each other. Wallace et al (2012) reported that the

permutation of millions of neurons distributing the signals simultaneously to create an

massive volume of electrical movement in the brain, can be perceived by consuming

complex medical tools such as an EEG which processes electrical levels over areas of

the scalp. Michalopolous et al (2011) reported that the electroencephalogram (EEG)

recording is a suitable tool for learning the functional state of the brain and for

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analysing certain syndromes. The mixture of electrical movement of the brain is

usually called a brainwave pattern because of its wave-like nature.

4.3.2 Overview of EEG Signals

EEG signals contain more relevant information about brain disorders and

different types of artifacts. Signals in the form of dataset are already loaded to the tool

so that it will be using those signals to plot the data and visualization of the time-

frequency domain plots which can be displayed all together. Basically the work will

be monitoring the EEG signals according to the placement of electrodes which are

called montages. After that the research work will observe the EEG signals to

recognize and eliminate different disease related artifacts. Then unwanted signal will

be subtracted by differential amplifier. Finally the work will proceed for the signal

filtering based on the different types of brainwave frequencies to diagnosis and

simulate variety of brain disorders by using MATLAB.

Figure 4.1 Overview of the EEG Signal Processing Technique

4.4 ANALYSIS AND DESIGN

4.4.1 Study of Existing EEG Hardware Techniques

Steps involved in the existing techniques: the electrodes are placed on the

brain by wires and electrical activities of the brain are recorded in a computer. It will

display the movement as a sequence of wavy lines drained as an image on the

computer screen. Patients need to lie down and close their eyes during the recording.

The recording might be motionless since there should be time to permit the patient for

widening and repositioning. Different things will be done by the patient during the

test to record the brain activity at that time. Such as taking breathe deeply and rapidly

for few minutes and looking at a bright, flashing light for checking the stimulation.

After recording the brain activities like the above mentioned process brain disorder

symptoms will be detected.

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4.4.2. Requirement Analysis

4.4.2.1. Functional Requirements

This research technique will provide the solution for the patients who will be

able to see their brainwaves while using this software called MATLAB; and then 2.

User will be able to see the data related to the processed brainwave signal. There are

different research techniques and features are defined as follows: EEG montages:

Montage means the location of the electrodes. EEG can also be examined with a

bipolar montage or a referential one. Bipolar means to use two electrodes on the scalp

on all the sides and reference electrode for one side of the brain. The referential

montage means only having a common reference electrode in both the side of the

brain. Channels: The electrical activity of the brain is conducted by wires from the

scalp and electrodes are placed by using EEG machine. The inputs to the hardware

EEG machine are then used to combine a montage, which is an exact organization or

array of electrodes that show the EEG signal. Sensitivity: Amplitude is the magnitude

of the EEG activity which is measured in microvolts (µV). It is determined by

measuring the brainwave deflection in millimetres (mm) at specified machine

sensitivity (µV /mm). Filtering: Low- pass filtering is used for smoothening the

brainwaves and high-pass filtering is used for sharpening the brainwaves in order to

make the signals more clearly to the viewer. Frequency Sweeping: Sweeping basically

reduces the complexity towards analysing the brainwaves for EEG signal processing.

It is possible for the new user and can also use these techniques and features with the

data through MATLAB for brain signal processing and other purposes.

4.4.2.2 Non-Functional Requirements

There are two non-functional requirements used in this research work first one

is the performance analysis for the MATLAB software tool which is efficient in

analysing and processing the signals in a proper way so it will be easier for the user to

observe the signals properly. Then the second one is the reliability checking tool used

for analysing the EEG signals, removing and recognizing the artifacts to process the

signal datasets.

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4.4.2.3 Background Design Requirements

EEG signal processing in built plug-ins under MATLAB environment, while

looking at all the possible technologies, libraries, platforms that will use in this

research work; it has seen that the most convenient programming language for this

work is inbuilt plug-ins which work under MATLAB environment.

4.5 SYSTEM DESCRIPTION

In this research work it has encompassed several features. Such by means of

EEG montages; Montage capitals the location of the electrodes. The EEG can also be

observed with a bipolar montage or a referential one. Bipolar means that just to use

two electrodes on the scalp on all the sides and for reference electrode one side of the

brain. The referential montage means only having a common reference electrode in

both the sides of the brain. In this part, we will be presenting how brainwaves will

differ according to the placement of electrodes.

i) Right Montages: Patient information has composed conferring to electrodes that

are located in the right side of the brain, so it will show the waves related to the right

side of the brain based on time-frequency analysis.

ii) Left Montages: Patient information has composed conferring to electrodes that are

located in the left side of the brain, so it will show the waves related to the right side

of the brain based on time-frequency analysis.

iii) Both Side Montages: Patient information has composed conferring to electrodes

that are located in the left and right side of the brain, so it will show the waves related

to the right side of the brain based on time-frequency analysis.

4.5.1 EEG Channels

The electrical activity of the brain is conducted by wires from the scalp and

electrodes are placed by using EEG machine. The inputs to the hardware EEG

machine are then castoff to comprise a montage, which is an exact preparation or

array of electrodes that show the EEG signal. In this research the work is dealing with

basically 20 channels of the brain because EEG hardware machine deals with only 20

channels of the brain. Each channel basically compares input data taken based on

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placement of the two electrodes. Upward deflection of the wave is defined as negative

and occurs when the first input data is negative with respect to the second input data

or second input data is positive with respect to the first input data. A descending

refraction of the brainwave is clear as positive then follows once first input data is

positive with deference to second input data or then second input data is negative with

respect to first input data.

4.5.2. Sensitivity

Amplitude is the magnitude of the EEG activity which is measured in

microvolts (μV). It is determined by measuring the brainwave deflection in

millimetres (mm) at specified machine sensitivity (μV /mm). The work have analysed

the brainwaves according to the collected sensitivity values of the patients, EEG

procedures are performed at a sensitivity rate of 7 μV /mm, such that a 10 mm

deflection of waves signifies amplitude of 70 μV.

This work ensure the dignified sensitivity values as 10 μV /mm, 15 μV /mm,

20 μV /mm, 30 μV /mm, 50 μV /mm because it is easier to determine the brainwave

patters with these values.

4.5.3 Filtering

Low- pass filtering is used for smoothening the brainwaves and high-pass filtering is

used for sharpening the brainwaves in order to make the signals more clearly to the

viewer. According to the patients EEG hardware data collected, this research work

have shown two types of filtering technique options such as – Low – pass frequency

filters and High- pass frequency filters For the low – pass frequency filters generally

setting the maximum range is till 1Hz and for high – pass frequency filters setting the

maximum range is till 70Hz because this the standard limit of filters.

4.5.4. Frequency Sweeping

Sweeping basically reduces the complexity towards analysing the brainwaves

for EEG signal processing. At last this work will sweep the signal to reduce the

complexity for the visualization of the brainwaves. Nolan et al (2009) reported that in

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this work will resolve using linear frequency extensive and it requires a stable rate of

frequency per interval. Essentially it is dignified as Hz/sec.

4.6 ARCHITECTURAL DESIGN FOR EEG ANALYSIS

Figure 4.2 System architectural designs

4.7 METHODOLOGY 4.7.1 Procedures i) Dissimilar EEG signals are composed as a form of dataset in the MATLAB;

ii) Load the data into the software for brain signal processing and then practice the

datasets;

iii) Remove and select the particular features for different EEG datasets;

iv) Classify the datasets conferring to the product features such as- montages,

channels, sensitivity, filtering and sweeping

v) Check the difference of dissimilar brain waves based on their characteristics,

vi) Select the specific montages such as – left side montage, right side montage or

both side montages to check change in different brainwaves.

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vii) Then select the channels out of 20 channels for viewing more detailed waveforms.

ix) By fixing the sensitivity values of the collected EEG data it will be setting filtering

range of the signals for high frequency (50 - 70 Hz) or for low frequency (0.1 - 1 Hz);

then it will change the values of sweeping also according to the data.

x) At last it will get the final EEG signal in waveform.

4.7.2 Flow chart

SELECT RIGHT/ LEFT / BOTH MONTAGES

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4.8 IMPLEMENTATION OF BRAIN SIGNAL DATA ANALYSIS AND RESULTS

The figure 4.3 shows the options for checking the exiting patient’s records by

selecting the first option and for diagnosis of different brainwaves forms selecting the

second option.

Figure 4.3 Sample results and other records page Figure 4.4 Brainwaves pattern The figure 4.4 displays the result obtained by the changes made in montage module

just by selecting right montage of the brain and different frequencies of brainwaves

can be easily determined.

Figure 4.5 Signals and frequency variation The above figure 4.5 displays the report of the test generated according to various

data which are collected from patients and they have been imported in to the program

so that it will be easier to undergo many changes according to different modules.

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4.8.1 Testing

Table 4.1 different patient’s data of montages

5.6014109e-001 6.9510397e-004 2.5127513e-001 4.3194860e-001 4.3065287e-001 8.8967125e-001 2.3849760e-002 1.2872483e-001 7.4064020e-001 3.9102021e-001

Table 4.2 different patient’s data of brain channels

4.8976380e-001 1.9324533e-001 8.9589157e-001 9.9089650e-002 3.4878481e-001 4.5134058e-001 2.4090500e-001 7.1504501e-001

Table 4.3 different patient’s data of brain sensitivity

5.7083843e-001 9.9685021e-001 5.5354157e-001 5.1545845e-001 5.7161573e-001 1.2218915e-001 6.7116623e-001 5.9958555e-001

Table 4.4 different patient’s data of signal filtering

1.5194708e-001 3.9710884e-001 3.7472247e-001 1.3111471e-001 8.8665840e-002 8.3825559e-001 5.8471862e-001 9.4810874e-001

Table 4.5 different patient’s data of signal sweeping

4.3390472e-002 6.9162515e-001 9.7898547e-001 2.8326790e-001 2.6296403e-001 6.8056620e-001 2.3365315e-001 4.5642536e-001

The overall research work consists of five modules which were completed in

their respective time frame. The implementation and functionalities procedures

seemed a daunting task, but were successfully completed to achieve the desired

objective. After the successful implementation of test results, this research work can

be applicable for monitoring alertness, coma and brain death; testing drug effects;

investigating sleep disorders; Investigating mental disorders; Locating areas of

damage following head injury, stroke and tumour and Monitoring the brain

development.

4.9 SUMMARY

The above mentioned research work has clearly demonstrated the concepts

about open source plug-ins, running under the platform MATLAB environment and

its ability to process biophysical data by different ways such as using simplicity of its

command line language or using the many MATLAB functions and the methodology

related to the analysis of the brain signal processing through MATLAB software

toolbox. It has been described in detail, the procedure in the modelling of EEG signals

and insight brain signals recorded during surgical procedure.