 Open Access
 Authors : Dr. V G Sangam , Sushrutha Bharadwaj M , Sahana Sundar Raman , Lakshmi A S, Prerana Ananda Murthy, Mohammed Faizan
 Paper ID : IJERTV9IS060814
 Volume & Issue : Volume 09, Issue 06 (June 2020)
 Published (First Online): 29062020
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Electroencephalogram (EEG), its Processing and Feature Extraction
Dr. V G Sangam, Sushrutha Bharadwaj M Department of Medical Electronics Dayananda Sagar College of Engineering Bangalore, India
Abstract This paper deals with the basics about electroencephalogram, its processing and feature extractions. Prominently used extraction methods such as Principal Component Analysis, Independent Component Analysis, TimeFrequency Analysis, Wavelet Transform have been discussed here along with mathematical representations. Software tools and their use towards EEG are highlighted.
KeywordsElectroencephalogram, tests, waves, processing, feature extractions, mean, standard deviation, power, variance, skewness, software tools

INTRODUCTION
An Electroencephalogram (EEG) is a medical routine that detects abnormalities in the brain waves, or in the electrical activity of the brain. During the test, electrodes are pasted onto the scalp of the patient. These electrodes are tiny metal discs with thin wires connected to the acquisition system and they detect tiny electrical charges that result from the activity of the brain cells. This is then amplified and appears as a graph on the computer screen, or as a hardcopy recording. The doctor/technician then interprets the reading. [1]
The EEG test is performed by an electroencephalogram technologist. It is done in the following way:[2]

You lie on your back on a bed or in a reclining chair.

Flat metal disks called electrodes are placed all over your scalp. The disks are held in place with a sticky paste. The wires protruding out of them connect to the acquisition system. This system coverts the recording/signals into EEG patterns that can be viewed on screen or printed onto a sheet of paper. These patterns look like wavy lines.

You need to lie still during the test with your eyes closed. This is because movement can change the results. You may be asked to do certain things during the test, such as breathe fast and deeply for several minutes or look at a bright flashing light.

You may be asked to sleep during the test.
Depending on the kind of activity and hence the frequency range it falls in, the EEG wave can be classified into beta, alpha, theta, delta and gamma waves. Their frequency ranges are as follows: [3]
Sahana Sundar Raman, Lakshmi A S, Prerana Ananda Murthy, Mohammed Faizan Department of Medical Electronics
Dayananda Sagar College of Engineering Bangalore, India
Waveform
Frequency Range
Activity
Beta
13 – 30Hz
Highly active brain activity and conversations
Alpha
8 13Hz
Very relaxed. Deepening into meditation
Theta
4 8Hz
Drowsy and drifting into sleep and dream
Delta
0.1 4Hz
Deep sleep with no dream
Gamma
30 100Hz
Hyper brain activity (great for learning)


ADVANTAGES OF EEG The advantages of EEG include: [4]

Functionally fast and are relatively cheap and safe method of analyzing the functionality of the brain

High precision time measurements

High resolution EEG technology available that can detect activities of even one millisecond

Mostly used as a noninvasive procedure

Easy and simple to use


DISADVANTAGES OF EEG The disadvantages of EEG include: [4]

The main disadvantage of EEG recording is poor spatial resolution

The EEG signal is not useful for pinpointing the exact source of activity. In other words, they are not very exact

It is difficult to differentiate between activities occurring at closely adjacent locations


APPLICATIONS OF EEG The applications of EEG include: [4]

It is mainly used in understanding properties of the brain and its associated areas

When on observation, it helps the doctors to monitor neural patterns in adults and infants. This will help them in detecting abnormalities

In epilepsy, EEG is used to locate areas of the brain and connect them to receive localization information

The feedback system in EEG has ample uses such as in that of psychological, physiological, and/or neurological disorders. This is called EEG neurofeedback

Many disorders as chronic anxiety, depression etc. can be found out using as EEG pattern


PROCESSING AND FEATURE EXTRACTION
OF EEG
EEG signal processing involves the following stages: [3]
independent components. Individual and independent components can be extracted from mixed signals by using ICA. In this manner, independence denotes the information carried by one component cannot be inferred from the others. [5]
Statistically this means that joint probability of independent quantities is obtained as the product of the probability of each of them. [5]
The ICA finds the unmixing matrix (W) and then projects the whitened data onto that matrix for extracting independent signals. [8]
Mathematically,
Let = Cov(X) and let X = AS, B = A1 Then,
Figure 1: EEG signal processing stages [3]
The feature extraction methods of EEG are as follows:

Principal Component Analysis

Independent Component Analysis

TimeFrequency Analysis

Wavelet Transforms

Principal Component Analysis:

B = W1/2 (2)
for some nonsingular W
Then, S = BX = W1/2X with Cov(S) = I and W are orthonormal. [9]
Therefore, operationally, = 1/2X data is sphered and then seek an orthogonal matrix W so that the components S
= W are independent. [9]
Independent Component Analysis helps in segregating the brain and nonbrain components from the acquired EEG
Principal Component Analysis (PCA) is a well
established method for feature extraction and dimensionality reduction. In PCA, we try to represent the ddimensional data in a lowerdimensional space. Such a representation would reduce the degrees of freedom and the space and time complexities. [5]
The objective is to represent data in a space that best expresses the variation in a sumsquared error sense. To segregate signals coming from various sources, this technique provides to be useful. It facilitates significantly if we know how many independent components exist ahead of time, as with standard clustering methods. [5]
A standard PCA when used as a data analysis tool involves a dataset of p number of observations for n number of entities or individuals. These data values define p n dimensional vectors x1, . . ., xp or, equivalently, an n Ã— p data matrix X, whose jth column is the vector xj of observations on the jth variable. [6]
Linear combinations are given by
signal. [7]

TimeFrequency Analysis:
The timefrequency representations, which map a one dimensional signal into a twodimensional function of time and frequency, can be divided into two main classes: linear and nonlinear timefrequency representations. [10]
The linear methods include the shorttime Fourier transform (STFT) and wavelet transform (WT). The nonlinear methods include the WignerVille distribution (WVD), the exponential distribution (E), and the reduced interference distribution (RID). [10]
For a function , its Fourier Transform is given by,
= ()2 (3) where () is the timedomain or temporal behaviour
and is the frequency behavior [11]
Timefrequency analysis involves the analysis of the
= (1)
intermediate signals that combine data of both and . It is
=1
where is a vector of constants a1, a2, . . ., ap [6]
Performance of PCA helps in minimizing the data and time required for computation. It reduces the dimension of the EEG data. [7]
B. Independent Component Analysis:
Independent Component Analysis (ICA) is another feature extraction method. This is used to convert random signals with multiple variables into one with mutually
given by, V (, ) where, it measures the strength of
frequency at time . [11]
They provide the right visualization of the EEG waves so as get the various frequency wave bands. [12]

Wavelet Transforms:
It is a mathematical transform that gives the time frequency representation of the signal. It is an alternative to the short time Fourier Transform (STFT). [13] Most of the feature extraction techniques for classification of EEG
waves include wavelet transforms. It is usually used in the preprocessing stage. [14]
An individual wavelet can be defined by, [15]
there is significant variation between both these set of values.
The variance helps in comparing the different
Then,
,
() = 
1
1/2
() (4)
dispersions of the various sets of the EEG data samples from their means. [17]
C. Standard Deviation: [18]
The measure of dispersion of a set of data from its mean
()(, ) =
() ( )
(5)
is called standard deviation. It is given by,
And CalderÃ³ns formula gives,
() = , ,,()2
(6)
= 1 ( )2
=1
=1
(10)
where Âµ is the mean of the signal
where [16]
,() = 1/2 () (7)
A common type of wavelet is defined using Haar functions. [15]
This transform is used for correct analysis of EEG. It could be seizure analysis, neuron potential modelling, etc.
[12] 

FEATURE EXTRACTORS

Mean: [17]
The ratio of summation of all the values of the signal and the total size of the signal is called the Mean of the signal. It is denoted by,
Figure 3: Analysis of Standard Deviation [18]
The figure above is a plot of the standard deviation values of normal and seizure affected individuals. The plot here shows that the standard deviations of the set of values
( ) = 1
(8)
have significant difference from one another.
=1
The standard deviation also helps in comparison between
where is the mean of the signal and {x1, . . .., xn} are the values of the signal.
Calculation of Mean help in analysing the weights of various sets of samples of the EEG data.

Variance:[18]
Mathematically, variance is a measure of statistical dispersion of a random variable. It is given by,
the different dispersions of the various sets of the EEG data samples from their means. [17]

Skewness: [18]
The lack of symmetry is measured by the skewness. It is given by,
3
3
=1
=1
= 1 [ ] (11)
= 1
( )2
(9)
where Âµ is the mean of the signal and is the standard
where Âµ is the mean of the signal
=1
deviation of the signal.
The value of skewness gives an interpretation on which side of the mean point the data set is distributed.

Power: [17]
The measure of amplitude of EEG signal is given by power of the signal. It is denoted by,
2
2
=
()
(12)
Figure 2: Analysis of Variance [18]
The figure above is a plot of the variance values of normal and seizure affected individuals. The plot shows that
Where X is the values of the signal and L(X) is the length of the signal.
The calculation of Power enables analysis of the strength with which the EEG data is occurring. This plays an important role in drawing conclusions about the subject.


SOFTWARE TOOLS
The software tools usually used for signal processing include MATLAB, Octave and SciPy. Of these, MATLAB has always been used as a promising tool for the processing.
[19]MATLAB
MATLAB is a software that can be used for algorithm implementation, matrix manipulations, display and plotting of various functions and signals, interfacing with other programs in other languages, etc.
EEG can be analysed directly in MATLAB by writing appropriate. But, a more effective and better way of processing EEG data in MATLAB would be using the EEG Lab toolbox. This interactive toolbox enables us to perform various operations on the both continuous and eventrelated EEG data such as Principal Component Analysis, Independent Component Analysis, 2D plotting of EEG signal, 3D plotting of EEG signal, power spectrum analysis, etc. [22]
NeuroView
NeuroView is a software that is designed to record and observe realtime EEG data. Other applications that are used to analyse EEG data can import the information from NeuroView. Programs like Excel can be used to view the data recorded by NeuroView as they are stored as CSV (CommaSeparateValues) files. [23]
BCI Companies
Brain Computer Interface (BCI) devices are used to send and receive signals between the brain and the external environment. BCI manufacturing companies include, NeuroSky, NeuroVista, EMOTIV, NeuroVigil, Nymi, AliveCor, SHL, FocusBand, Atentiv, BioBeats and Champalimaud Foundation, etc. [24] For usage in biomedical and related equipment for easy acquisition and other worthwhile factors, NeuroSky products are preferred. [25]
Their products help analyse biometric data in a very easy and practical way. The solutions provided help to motivate people and make their lifestyles better. [26]
ThinkGear
NeuroSky uses a dry sensor technology. This is used for the measurements, amplification of EEG signals and brainwaves. These are also used to filter and analyse the brainwaves and EEG signals. This technology is called ThinkGear. This technology helps respond to persons mental activity aptly. [27]
This technology is used in a device named Brainsense by Pantech Solutions. This is a single channel wireless headset connected to the system using Bluetooth. The activity of the prefrontal lobe is measured thus acquiring the subjects pre frontal cortex EEG data accurately. [28]

CONCLUSION
EEG is a neurological test that uses an electronic monitoring device to measure and record electrical activity in the brain. It is the key tool in the diagnosis and
management of epilepsy and other seizure disorders.
[29] Interactive MATLAB tools, NeuroView and other similar software are used for processing continuous and eventrelated EEG, MEG and electrophysiological data using ICA, PCA and other methods including artifacts rejection.ACKNOWLEDGMENT
We like to thank all the staff of the Department of Medical Electronics and the management of Dayananda Sagar College of Engineering who made it possible for us to come up with this paper.
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