Using K-Nearest Neighbors and Support Vector Machine Classifiers in Personal Identification based on EEG Signals

Using K-Nearest Neighbors and Support Vector
Machine Classifiers in Personal Identification based
on EEG Signals
Shaymaa adnan Abdulrahman
Department of computer
Engineering, Imam Ja'afar Al-Sadiq
University, Baghdad, Iraq
PhD Student at Ain Shams University,
Egypt
Shaymaaa416@gmail.com
Mohamed Roushdy
Faculty of Computers &Information
Technology, Future University in Egypt,
, New Cairo, Egypt
Mohamed.Roushdy@fue.edu.eg
Abdel-Badeeh M. Salem
Department of Computer Science, Ain
Shams University ,Cairo,Egypt
absalem@cis.asu.edu.eg
Abstract—In the present paper, electroencephalogram (EEG)
data have been used to human identification by computing
sample entropy and graph entropy as feature extractions. Used
two classifier types, which are K-Nearest Neighbors (K-NN) and
Support Vector Machine (SVM). Python and Matlab software
were used in this study and EEG data was collected by UCI
repository . Matlab used when Thirteen channels was applied as
feature extraction . The experimental results show that, Python
software classifies the EEG-UCI data better than MATLAB
environment software where the accuracy of KNN and SVM
were 85.2% and 91.5% respectively.
Keywords- Biometrics, K-Nearest Neighbors, Support Vector
Machines , Electrocardiograph Signals, Python, Human
identification
I.INTRODUCTION
Human identification can be considered as the first and
most important step in a validation process for use in security
system. The word "identification" in some cases is being
confused with two other security associated terms which are
authorization and authentication . In fact,. identification can
be defined as the detection ,or identification of a certain
persons authentication is defined as the verification of the
individual's claimed identity and the . authorization is defined
as the official approval [1] . Human identification with the use
of a special behavioral - physiological feature of a personals
referred to as the biometrics. Usually biometrics that are based
on the EEG signals have increased in research . The reason for
this increase is that it transmits information and data from the
human brain . This information is individual so that it can be
used as a biometric [2] . The EEG are among the most reliable
measure and hard to reproduce. In addition not possible to be
stolen or obtained under , threat . There are a large number of
investigations were carried out regarding the identification of
identity through the use of number of programming
environments such as MATLAB , Python ,JAVA, C# ,….. etc.
Previous studies in this research contain different methods for
extracting data with different methods for the classification
process , especially the use of the python environment .
The contribution of the present study is a comparison
between Python environment and Matlab software when two
different methods such as ( KNN and SVM) as classifier for
identifying a person . The previous work has been used overall
process in Matlab software . While for implementing and ,
evaluating the suggested technique , utilized two distinct
programming languages : preprocessing and feature
extractions in Matlab software while classification and
evaluation in Python -Scikit-learn Toolbox . For access to
built-in machine learning functions in scikit-learn package [
12].
This paper has been structured as: Section 2: Background
Section 3:Overview data analysis with python environment
Section 4: Proposed work , Section 5: Result and Discussion ,
Section 5:Conclusion
II. BACKGROUND
According to (Hu, Jianfeng ,2018)[3] suggested EEG to
recognize gender . Four different approach was used like
(fuzzy entropy, sample entropy, approximate entropy, spectral
entropy) as feature Extraction . While different types of
classification was applied with EEG signals collected from
twenty eight subjects . While (Shaymaa Adnan Abdulrahma ,
et al 2019) [1] used electroencephalography to human
identification . Sample Entropy and Horizontal Visibility
Graphs used as feature extraction . The accuracy with
Horizontal Visibility Graphs had a much better than sample
entropy when used Machine Learning Repository (UCI) as
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 18, No. 5, May 2020
29 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

dataset . (Galbally et al. 2012)[4] investigated twenty two
images quality measures (like: occlusion, focus, motion, and
pupil dilation) . Sample entropy was implemented as feature
extraction . Best feature is m chosen via the sequential
selection of the floating feature for feeding a quadrat
discriminant classifier . Smart-Box for Face Recognition was
implemented according to (AkhilGoel et al , 2018) [5] .
Usually Smart-Box consists of 3 adversarial perturbation
modules such as (attack generation, detection, mitigation) .
Images of a single individual from the Yale Face Database.
The accuracy was 45 and identification more than 90%
when deep-Fool decreases algorithm was implemented . A
novel BCI P300 used to collected dataset when used EEG
signals for identification was suggested by (Santosh Thakur,et
al 2020) [6] . nonparametric weighted feature extraction
approach has been implemented for the extraction of features.
KNN classifier based rule set was applied to identification.
Shown Table 1
TABLE I. TABLE 1 : PREVIOUS WORKS FOR HUMAN IDENTIFICATION
The above table shows some of the different methods in the
classification process . Data set was taken from different types
of biometric like( face , EEG, iris ). Some of this data was
taken directly from the user by using electrodes to measure
brain signals . During the study of previous works . The
accuracy ranged between 92% to 97% .
III .OVERVIEW DATA ANALYSIS WITH PYTHON ENVIRONMENT
Python is one of the programming language that can be
used in the mathematical and statistical calculations process
and for many operation such as classification pre-process ,
data extraction , prediction …..etc .[7] . Python is considered
to be one of the simple and powerful programming languages.
It has bridged the gap between programming such as C and
shell program . One of the advantage of Python is it fast
learning . The interpreter of Python is extended easily with
new data types and functions which are implemented in C and
is suitable as well, as one of the extension. languages for the
highly customizable , applications of the C programming
language like window, managers or editors, Website
( https://www.python.org/) . Python is available for a variety
of the OS, like Amoeba, UNIX, Apple Macintosh OS, and
MS-DOS. Moreover, it contains different high-level libraries
(for the detailed benefits of Python python.org/about/) ,, like
Scipy. (scipy.org/) allowing the user to be running MATLAB
codes , following a little alteration . As for the signals and
neuroimaging such as EEG , ECG ,EMG ,….etc.
(http://nipy.sourceforge.net/) , it contains a set of toolbox
dealing with this type of data . We will mention some of these
function which are :
1)Scikit-learn Toolbox: refer to python module contain large
number of algorithms used to machine learning . Represent
this package is focus on delivering the machine learning to the
non-specialists through the use high level general-purpose
languages [7 ]. The focus has been emphasized on the
performance ; documentation , ease of utilization and .
Consistency of the API . It has minimum dependency values
and has been distributed under simplified license of the BSD,
which encourages its utilization in the academic as well as the
commercial settings (scikit-learn.sourceforge.net) . Scikit-
learn python package can be defined as rich environment for
providing state-of-art implementation of numerous
algorithms of machine learning with, keeping the easy-to-use
interface closely integrated . The scikit-learn differs from
other , tool-boxes of the machine learning in Python for ,
many reasons:
• It has incorporated compiled, code for effectiveness,
in contrast to the MDP [ Zito etal., 2008] [8] and
pybrain [Schaul et al., 2010] [9] .
• It has been distributed under the license of Berkeley
Software Distribution (BSD)
• Focus on imperative , programming .
• Has the ability of evaluating the efficiency of the
estimator or selecting parameters
• with the optional use of the cross validation, which ,
distributes the computation to numerous cores .
• It is only, dependent upon the numpy and ,scipy for
the facilitation of the easy distribution
Authors Feature
extraction
Classifier Dataset Biometric
type
Accuracy
[Hu,
Jianfeng,
2018]
sample
entropy
fuzzy
entropy
approximat
e entropy
spectral
entropy
KNN
RF
DT
QDA
28
subject
by using
electrode
EEG
0.99
0.949
0.961
0.966
[Shaymaa
Adnan
Abdulrahm
an, et al
2018]
Sample
Entropy
+
Horizontal
Visibility
Graphs
KNN UCI
Database
EEG 92.6%
97.4%
[Galbally
et al. 2012]
Sample
entropy - UCI
Dataset
Iris
[AkhilGoel
et al ,
2018]
- Yale
Face
Database
Face
recogniti
on
[Santosh
Thakur et
al ,2020]
non-
parametric
weighted
KNN 20
subjects
by using
electrode
EEG 92.46%.
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Usually Scikit-learn includes a large group of the statistical
learning approaches, like (unsupervised and supervised) . In
addition its applications to the neuro-imaging information
has given versatile tools for studying human brain. There
wide variety of tools found inside the Scikit –Python[13] .
2)PyEEG Toolbox: is an open source module of Python for
the EEG which has been utilized for feature extractions. In
addition this Toolbox applied for the analysis of other
physiological signals which have the ability of being
considered as time series like MEG signals , representing the
magnetic fields which have been induced, by the neural,
electrical activity currents . One of the advantage of this
Toolbox [14] . The framework PyEEG has been focused only
on the extraction of the features from the segments of
EEG/MEG. It contains no functions which are utilized for
importing information of different formats or exporting
features to a process of the classifier. Shown figure 1 .
PyEEG contains numerous parameters and different
algorithms for one characteristic such as Relative Intensity
Ratio and Power Spectral Intensity (PSI), Petrosian Fractal
Dimension, Higuchi Fractal Dimension
(https://www.hindawi.com/) .Numerical values of the feature
which has been obtained through using PyEEG can differ
from the features which have been obtained by other tool-
boxes. Therefore programmers must utilize the non-default
values for parameters so as to meet their requirements.
Figure1 Framework of PyEEG
3)Wyrm Toolbox: refer to an open source BCI toolbox in
Python . These toolbox can implemented to a wide range of
neuroscience problems . It analyzes and visualizes
neurophysiological data in real time setting
(https://github.com/bbci/wyrm) such as on line Brain-
Computer Interface (BCI ) application. Shown figure 2 . Row
contain number of channels like (F7, F8, C3, C4 , ………),
while Colum refer to real time[10] .
Figure 2 :Visualization of the Data object and attributes of
Wyrm Toolbox
4)MNE-Python Toolbox : is an open source package
software . It used for (information / data) preprocessing or
statistical analysis or estimation of the functional connectivity
between the distributed areas of the brain and Source
localization . One of the advantage of this MNE is that it can
access pre-processed data in an easy and fast . This feature
helps users to quickly reproducibility approach and methods
by other researchers (http://martinos.org/mne.) . MNE-
Python has a distinctive feature that it gives a modules with
the graphical . user interfaces(GUIs) , valuable to inspect ,and
explore data[11] . Shown figure 3
Figure 3: GUI applications which are provided MNE Python
5)PyMVPA –Tool-box : is also an open source software built
in Python environment using for the application of
classification based analysis technique to f-MRI data-sets.
Usually this toolbox applied for accessing the libraries which
have been written in many different computing environments
and programming. Languages . to the machine learning
package interfaces [15] . The function inside this toolbox help
researchers for easily conducting the analyses of noise
perturbation. The tool-box of the PyMVPA is a free open
source software , which was available from the website
(www.pymvpa.org) .
IV. PROPOSED WORK
A. Data set
The data-set utilized in the presented study was obtained
from the repository of the UCI [16][17] . The data-set includes
twelve input feature vectors , and a single target vector . Those
input vectors have been acquired from the application of
wavelet packet analysis . On the original signal in the band of
the frequency (7Hz- 13Hz). The target vector is the planning
or relaxed state . The training data includes ( 91 samples) and
EEG series
Non feature
extraction
Feature extraction
functions
Feature values
PyEEG
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testing data includes ( 91 samples) as well . There has been a
total of (182 samples ) . Due to the fact that dataset which has
been utilized in [18][19] has been 50% test data and 50%
training data a similar number of testing and training data
have been utilized in our study. When applying this
experiment. Thirteen channels such as
(AF8,C1,C2,C3,C4,CP1,CP5,CP6,FC5,FT7,P8,PO8,PZ ) were
used to extract feature . Figure 4 display the proposed
research methodology of this work.
Figure 4 diagram of proposed search
B. Feature extraction
Sample-Entropy is one of the way in which the process of
extracting information from raw data . It has proposed since
2000 by (Moorman and Richman) in order to improve the
approach of the approximate entropy, which is a non-linear
dynamic parameter for measuring the Complexity of a
sequence [20]. Sample-Entropy consist of three parameters
as input . Which are r represents the criterion of similarity( m)
represents the embedded dimension . and (n) represents time
series length [19]. In the present paper two parameters were
applied : Se1 m=2.0, r=0.15 and Se2 :m=2.0, r=0.20 of
every epoch of EEG signals are extract. These same values
were used due to a compare this experience with previous
studies [ 19] . We applied Sample-Entropy with 13 channels
as feature extraction . Sample entropy refer to the negative
natural logarithms when provided with the conditional
likelihood , meaning that any. 2 sequences which are same for
the (m) points stay. unchanged at the following point, in
which ( r ) represents the similar criterion and (m ) represents
the data segment’s length [21]. The mathematical application
of the Sample-Entropy process may be represented as follows:
Shown question 1 according to [19].
…….(1)
Where
refer to probability of the 2 sequences, matching for
(m) points .While stands for the likelihood for 2
sequences to be matching for m+1 points . .
, N data point from time series
x(n)=x(1),x(2) ,x(3)…..x(n) . m vectors represented as
for 1 k N-m+1 at the ith
samples .There are many techniques to use for the calculation
of the Graph- Entropy approach dependent on edges or vertex.
This work know the ( Graph- Entropy ) [22] with the formula
of Shannon’s entropy (Clarke 1968). The Shannon’s entropy
can be define in the equation 4.
Graph- Entropy =
where p(k) is the probability of i
C. Classification Tool
C.1 KNN Classification
One of the type of techniques used in supervised learning
method is k-NN. The basic principle of this method is to
classify the data by calculating the k closest neighbors to a
Data-
Set
Accuracy
A
F
8
C
1
C
2
C
3
C
4
C
P
1
C
P
5
C
P
6
F
C
5
F
T
Channels
MATLAB
Environment
Feature extraction
Sample-
Entropy
Graph -
Entropy
Python
environment
SVM K-NN
Classification
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data point . In addition assigning the point to the most
common class among those k neighbors . Euclidean distance
was applied for distance metric .
Euclidean distance X1, X2 = …. (5)
Where X = (x11, x12, …, x1n) and X2 = (x21, x22, …, x2n)
KNN method were trained through the (sklearn.
Neighbors)[23] . K-Neighbors classifier module of python
library scikit-learn with the use of the parameter below:(I), the
Number of the neighbors . The number of neighbor k is (1)
and (5 ) fold cross validation was utilized for calculation of
the precision. The (python software)based implementation
code to find the optimal values of the parameter for the model
of the K-NN has been presented in figure 5. While Python
based implementations of KNN, classification model has
been illustrated in the figure 6[29].
K-NN (OPTIMAL PARAMETER )[29]
#/ Configuring the matplot library for drawing in-line graph on the web-page/
/get_ipython().magic('matplotlib inline')/
# Importing needed packages
/from the sklearn. model_ selection importing thetrain_test_split
importing the pandas as pd/
from the . sklearn. neighbours .importing the K-NeighborsClassifier
from the. sklearn. metrics importing the confusion . matrix
from the sklearn. metrics importing the classification. report
from the sklearn model -selection importing the GridSearch-CV
importing the -numpy as np
# Loading the EEG EyeState data-set from the file of thecvsto pandas
eeg = pd.read_csv('EEG-EyeState.csv')
# printing few records from EEG EyeState data-set
printing(eeg.head())
y = eeg.iloc[:,12]
# Extraction of the Independent (Prediction) variables (X) and Dependent
(Predicted) Variable (Y) from the EEG EyeState data-set
X = eeg.iloc[:,0:12]
# Computing summary statistics and round them to 2 digits
;eeg.describing().rounding(2).transposing()
# Computing corramongst all of the variables (Independent as well as
dependent)
eeg.corr().rounding(2)
# Plotting of scatter matrix amongst all of the independent variables
With the use of the dependent one
sm = pd.scatter_matrix(eeg, c = y, figsize = [20, 20], s=150,marker = 'D')
# Splitting thefull Data-set to Training data-set (50%) and Testing data-set
(50%)
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, y,test_size=0.5,
random_state=21, stratify=y)
# The count of the neighbors should decide from 1 to 10inclusive
param_grid = {'n_neighbors': np.arange(1, 10)}
Figure 5 : optimum k - parameter by using Python
KNN –Classification[29]
#The creation of the reference of the K-NN classifier
knn = K-Neighbors Classifier()
# Performing the search of the grid with the use of the K-NN approach and
finding the optimal neighbors’ value on which the approach will be giving
high precision with the cross validation = 12
k-nn_cv = GridSearch-CV(k-nn, param_grid, cv=12)
# Building K-NN based model of classification
K=-nn_cv.fit(Xtrain, Ytrain)
# Printing the optimal value of neighbour and the optimal score which is
accomplished by the training set K-NN model
/Printing (k-nn_cv.best_params_)
/Printing (k-nn_cv.best_score_)
# Performing prediction (i.e. classification) on the testing data-set
y_pred = k-nn_cv.predict(Xtest)
# Calculation of the prediction score on the testing data-set
k-nn_cv.score(Xtest, ytest)
# Computing the confusion matrix according to the actual v.s. the predicted
classon the testing dataset
print(confusion. matrix .(Ytest, Ypred))
# Computing the Classification according to the actual v.s. predicted class on
the testing data-set
Printing the (classification _report(Ytest, Ypred))
Figure 6 : pseudo-code of K-NN Classification
C.2 SVM Classification
The second technique used in this study is SVM. It is
considered one of the widely used methods in the field of
supervised learning approach. SVMs perform classification
through the detection of the hyperplane maximizing the
margin between both classes, notice, linearly separable and
binary. Support vector machine technique is proposed by
(Cortes and Vapnik) [24] . This method works on the basis of
reducing structural risk . SVM classifier with RBF kernel
were applied on two different binary classification problems in
this study . Shown the question (6) that is represented SVM
classifier . The classifiers were implemented using the open-
source scikit-learn library for python [25][28].
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Where and
Where 'w1' is the normal to separating hyperplane which is
represented by( ). For a set of data points ( ,) in which ( =
+1,−1) the margin between 2 classes is given by: . The
optimum margin, is computed through minimizing the
problem of the constrained optimization that is additionally
addressed through the reduction. It to the issue of the
quantization programming optimization which will yield.
Python Scikit-learn toolbox based implementation of SVM ,
classification model has been illustrated in the figure 7[30]
.
Pseudo-code (SVM scikit-learn-Python)[30]
Importing the numpy as np
Importing the pylab as pl
from sklearn import svm, data-sets
EEG = data-sets.load_EEG()
X = EEG.data[:, :2]
Y = EEG.target
h = .02 # the size of the step in mesh
# creating an SVMs instance and fitting out the -data. The data is not scaleddu
e to the need for plotting support vectors
C = 1 # SVMs parameter of regularization
svc = svm.SVC(kernel='linear', C=C).fit(X, Y)
rbf_svc = svm.SVC(kernel='rbf', fit(X, Y)
poly_svc = svm.SVC(kernel='poly',).fit(X, Y)
lin_svc = svm.LinearSVC(C=C).fit(X, Y)
Xmin, Xmax = X[:, 0].min() - 1, X[:, 0].max() + 1
Ymin, Ymax = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(Xmin, Xmax, h),
np.arange(Ymin, Ymax, h))
titles = 'SVC with RBF kernel',
for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):
# Plotting decision boundary, by assigning a color forevery one of the point
s in mesh [Xmin, Mmax]x[Ymin, Ymax].
pl.sub-plot(2, 2, i + 1)
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()
Z = Z.reshape(xx.shape)
pl.contourf(xx, yy, Z, cmap=pl.cm.Paired)
pl.axis('off')
# Plotting training points as well
pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)
pl.title(titles[i])pl.show()
Figure 7: pseudo-code of SVM Classification
V. RESULTS AND DISCUSSION
In comparison with other common programming
languages like Python and MATLA . These programs are
friendly programming languages that contain similar
characteristics and feature . Although there are similar
feature , some of the advantages of both MATLAB and
Python [27] . Table 2 illustrates these feature[31].
Table 2 comparison between Python and MATLAB software [31]
MATLABPython
- Simple computing
environment
-friendly for learn
-Fast Debug
-easy and fast code
-can be abstract out a lot of
implementation details
-In addition simple for matrix
operations
- not good to manage free
package
-Open source
-In Python , it need to add
specific libraries for mathematics
-requires function calls when use
matrix
Such as
>>> X = numpy.array()
>>> W = numpy.array()
>>> X.dot(W)
Or >>> X@W (Python 3.5)
Through the comparison between MATLAB and Python as
per the above Table , we found that in implement machine
learning algorithms is easy and simple compared to Python/
NumPy (library to work with array structures vector equations
with the use of the linear algebra or Scikit-Toolbox with basic
machine learning tasks . MATLAB can be better than
Python in relation to the introductory courses of
computational neuroscience . Python may be considered
ideal for low-resource institutions . (for the benefits of
Vol. 18, No. 5, May 2020
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Python, read (www.python.org/about/) for more detailed
information). like Scipy, (www.scipy.org/) allowing the user
running the, codes of MATLAB following a little of
modifications . Python - scikit- learn toolboxes used for train
and test . Split the data samples to 2 groups: 50% of the data
have been utilized for training while 50% percent for testing .
MATLAB environment was used Sample –Entropy and
Graph- Entropy used as feature extraction . While Python
software applied K-NN and SVM as classification . According
to accuracy . Our results demonstrate that used Scikit-learn
which has been used in the classification process better than
Matlab according to accuracy . Table 3 : refer to comparing
the result obtained in this experiment with the previous
study.
Table 3 : summarizes all results
Platform
classes
Data-
set
Number
of
channels
Feature
extraction
Classifier Accuracy
MATLAB
Software
(previous
work )
[Shaymaa
Adnan
Abdulrahman
,et al 2020]
[19]
UCI
13
(AF8
C1
C2
C3
C4
CP1
CP5
CP6
FC5
FT7
P8
PO8
PZ
Sample
entropy
+
Graph-
Entropy
K-NN 83.7%
SVM
90.8%
Python
Software
(Proposed
work)
UCI
13
(AF8
C1
C2
C3
C4
CP1
CP5
CP6
FC5
FT7
P8
PO8
PZ)
Sample
entropy
+
Graph-
Entropy
K-NN 85.2%
SVM 91.5%
Through table 3 illustrates the use KNN and SVM as
classifier. According to the suggestion in this experiment is
the use of Python in classification process and Matlab in
feature extraction operation . The accuracy was 83.7% when
used KNN, and 90.8% with SVM classifier . This result is
considered the best comparison with the previous work
referred to in this research . Especially that the same feature
extraction algorithm was used and for the same data-set.
VI. CONCLUSION
In the present study, a detailed analysis of signals such as
Electroencephalography 13 channels data as classification
class is carried out and compared to result which have been
provided in [19] on an identical data-set . Sample entropy and
graph entropy used as feature extraction . This step was
executed using MATLAB environment . In comparison with
the Graph- Entropy and Sample -Entropy has higher
robustness to the noise compared to ( log energy entropy ,
spectral entropy , kroskov entropy) . In addition , highly
independent of data , series length . Sample-Entropy doesn’t
perform the counting of the self – match. Thereby, the bias has
been eliminated . A lower Sample- Entropy value will indicate
higher, self-similarity in time series. For evaluating of our
approach , the KNN and SVM was implemented using a
second programming language . This software is Python .
Especially Scikit-learn Toolbox . The results show that ,
Python software classifies better than Matlab environment
software with KNN and SVM , where was the accuracy 85.2%
and 91.5% respectively . We found that simple algorithms can
give better result compared to complex algorithms . The
reason for this, some programming languages are function
inside toolbox built in a way that can give us better results in
many respects such as implementation time , accuracy ,
maintenance. In addition the specification of the device on
which the experiments are conducted according to the
processor speed , type and version have an effect on the also
results . In the future we can use two different languages of
programming to human identification but using another type
of biometric like (iris , voice , face ,…etc) to show if it gives
better accuracy when using the same two programming
languages.
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2019
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