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International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
18
A SURVEY OF BIOMETRICS USING
ELECTROENCEPHALOGRAM EEG
Shaymaa Adnan Abdulrahman, Wael Khalifa,
Mohamed Roushdy, Abdel-Badeeh M. Salem
Abstract: Biometrics has been defined as the inevitable recognition of users
according to the quality acquired from behavioral and/or physiological
properties. Through few years, some works have explore the biometric utilize of
human /animal brain signals that, for various reasons, have traditional received
little attention by the security community. Bio signals i.e. Chemical or electrical
signals estimate some of the parameters or variables of the human's body form
a significant class of the signals which include electromyograms (EMGs).
electroencephalograms (EEGs) and electrocardiograms (ECGs).
(EEGs)-based biometrics uses a little intra-personal and substantial inter-
personal distinction between individuals' brainwave designs. A lot of
organizations which include security sectors, including high-security
government, banks and police departments give divergence degrees of
precision and accuracy.
Keywords: Biometrics, EEG Bio signals, Machine Learning
ACM Classification Keywords: 1.2 Artificial intelligence, I.3.6 Methodology
and Techniques, J.: Computer Applications
Introduction
The electrical property of a brain is being recorded by EEG signals using a
series of electrodes fixed on a scalp. These signals are processed and
converted to a signature Hence, biometric signature (mono- or multimodal) is
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
19
being utilized for recovering patient's medical record in an appropriate form: the
master template and one read locally are compared by the system. In case
there is a match between them, the respect medical record will be redeemed.
This procedure is wholly carried out manually but as well is done automated,
that is. the biometric data are provided automatically by the user( Camara, C. et
al. 2015 ). The brain contains a number of neurons generated from a neuronal
activity of electrical signals. Electrode sensors are used to acquire brain
electrical signals. Brain signals are grouped into two types in obtaining activity,
that is. electrical and magnetic signals (such as EEG signal) and metabolic
signal ( Jana, Gopal Chandra et al. 2017 ) . EEG has several characteristics
that are useful for this issue; these include a higher time resolution that can
open a window for easier visibility of the brain dynamics (Ma Lan et al. 2015 ).
This condition can be used as a good biometric( Matyas, Zden’ek 2000 ).
Universality: This implies that an individual needs to possess characteristics. It
is not realistic to obtain 100% coverage because there are people without
fingers, mute people or with injured eyes. These situations should be catering
for.
Uniqueness: This implies that two individuals should not be in the same
biometric characteristics.
Permanence: This implies that there should be no variation in characteristics
and with time Collectability: This implies that the measurement of
characteristics must be done quantitatively and obtaining the characteristics
should be simple
Performance: This means the achievable identification/verification, resources,
accuracy environmental or working states needed to obtain an acceptable
accuracy.
Acceptability: This shows the extent people are ready to embrace biometric
system.
Circumvention: This explains the difficulty faced in fooling the system through
fraudulent techniques.
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
20
Biometrics indicate to the automatic identification of human based on features
obtain from their physiological and/or behavioral characteristics.. Biometric
technology is defined as any method that reliably utilizes tangible of these
physiological or behavioral characteristics for distinguishing one individual from
another. The origin of biometric technology is traceable to about several
thousands of years back ( Mu, Zhendong 2015 ) (Yanushkevich and Shmerko
2009) .biometric system can be performed using two different types of mode
including authentication and enrollment. In the latter, the biometric system
changes the individual's biometric characteristics into a digital form and stores it
in a storage system (Mani and Nadeski 2015).However, the biometric system
can be utilized for an identification or verification process in authentication
mode. During the verification process, the biometric system verifies an
individual's identity by comparing the recorded characteristics with the template,
show Figure 1.
Figure 1 biometric system process
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
21
In biometrics, there are a lot of bio-signals used in emotion recognition; these
include respiration changes, Electromyography (EMG). Blood volume pulse
(BVP), electrocardiogram (ECG), Skin temperature, and Electro dermal
response (EDR). Hence, the bio-signals for emotion detection is becoming an
interesting domain for human-computer interaction(Nawasalkar, R. K. et al.
2013 ) . Biometric characteristics are often categorized based on behavioral or
physiological nature. The latter is related to shape and color of the body. The
examples include retina and his recognition. Fingerprint, hand geometry, palm
veins, DNA, face recognition, and palm print. However, behavioral
characteristics are related to the behavioral patterns of a person which include
voice, gait and typing rhythm ( Ross, Arun A. 2007 ) (Yang, Su 2015 ) The
signal such as EEG is refer to often practical capture strategy that can be
applied in biometrics in order to of the improvement in its hardware system
equipment . it is individual personal . In addition , the brain wave signal is
biodynamic and you can process and confirmation of aliveness for particular
individual .for this reason it cannot be duplication such as most of the other
physical biometric mechanism (LIEW SIAW HONG 2016).usually signals like
(EEG, MEG ,ECG,…etc.) using for many sector such as security part ,in this
sector three type of authentication are applied consist of our information such
as user name ,password ,PIN, and sometimes piece of personal information(like
mother's maiden name) this is indicate biometric (Liu and Silverman 2001)
.while in the medical sector, the EEG can be used for detection and Diagnosis
Such as (sleep disorder ,brain disorder ,while you can utilized these signals to
prevention such as (smoking ,motion sickness). Medical institution began to
recorded biometric human information to avoided and control hacking( Poulos,
Felekis et al. 2012 ). Recognition of human patients entering through the doors
to the hospitals is big challenge facing the medical part . Biometric signature is
begin applied as the tools to get back and patients' medical record in general
form ,for this reason to control these records (patients' medical record) by link
between the medical records and patients' information biometric ,thus home
health care or disabled people can be improve by using biometric (Shakil et al.
2017).
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
22
In this study, we investigate various techniques and experiments that were developed for using
EEG signals as a user identification characteristic.
Anatomy of brain
The human brain is a three-pound organ that directs every parts of the body
elucidate information from outside to enrich the soul and mind, intelligent,
memory, emotion, and creativity are part of the several factors controlled by the
brain are secured in the skull. The brain comprises of brainstem, cerebellum
and cerebrum. The brainstem is a relay center between cerebellum and
cerebrum connected to the spinal cord. The brain receives information through
five senses: hearing sight, touch, smell, and taste. The nervous system is
divided into peripheral and central. The central nervous system (CNS)
comprises of the spinal cord and brain. However, the peripheral nervous system
(PNS) is composed of spinal nerves that branch from the spinal cord and cranial
nerves that branch from the brain. The PNS includes the autonomic nervous
system which controls vital faction such as secretion of hormones, breathing,
digestion and heart rate. However, the purpose of the bony skull is to protect
the brain from injury. The shall comprises of 8 bones that fuse together along
suture lines, these bones include the one frontal, two parietal, ethmoid, two
temporal, occipital, and sphenoid (Fig 3) (Mayfield 2019).The cerebrum is the
greatest portion of the brain that composed of right and left hemispheres. it
performs the higher faction like interpreting touch vision and hearing as well as
speech, reasoning, emotion, learning and fine control of movement. The
cerebellum is located under the cerebrum, its function is to coordinate muscle
movements, maintain posture and balance. The brainstem includes the
midbrain, pons, and medulla, its perform many automatic actions such as
breathing, heart rate, body temperature, wake and sleep cycle, vomiting, and
swallowing (Forstmann, Keuken, and Alkemade 2015) . Figure 2: Human brain
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
23
Figure 2- major internal of the Human Brain
Processing Techniques
In this section, several methods used human identification based on EEG
Signals are vividly discussed.
In ( Camara, C. et al. 2015 ) the authors proposed the utilization of
electrocardiograms (ECGs) in identifying individuals. Most of the ECG-based
biometrics systems choose facts (fiducial features) from the ECG wave
property. This study proposes the utilization of non-fiducial features through the
Hadamard Transform (HT) with a known dataset, especially MIT-BIH Normal
Sinus Rhythm Database. This comprises of long-term registration of 18 subjects
undergone at Boston's Beth Israel Hospital. This dataset was used due to the
fact that there were no significant arrhythmias in the subjects. Hence, there was
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
24
no bias in the subjects that can enhance the identification task. The initial stage
comprises of data acquisition that invokes one or more signals based on
whether the system is mono- or multi-modal. In general , bio signals are read by
placing a series of sensors over the subject (chest or head). Furthermore the
pass-band filter with a range of 0.67 and 45 Hz is used in the second stage. The
main importance of this transform is its higher efficiency compared to others. In
addition, the transform usage showed that compressed signal can be reserved..
There are 256 coefficients generated from the computation of HT over this
signals. The two added features in this study were Log-Encrg' entropy (ELE)
and Shannon entropy (ESH). The classifier utilized a K-NN algorithm as follows:
true positives (TP) rate, false negative (FN) rate, false positive (FP) and true
negative (TN).
(Ma, Lan et al. 2015 )Proposed the utilization of convolutional neural networks
(CNN) to analyze the performance of individual's best, uniqueness and carried
out the classification using EEG data obtained from Resting State with Closed
Eyes (REC) and Resting State with Open Eyes (REG). There are two Resting
state EEG with closed eyes (REC) and Resting-state EEG with open eyes
(REO).Ten subjects were analyzed for 55 seconds individually for both REC
and REO.The obtained results showed that CNN-based joint-optimized EEG-
based Biometric System generated an improved level of accuracy of
identification (88%) for the 10-class classification.
In (Mu, Zhendong, 2015 ) stated that Rough set theory has been successfully
employed in different information mining fields. Because a lot of researchers are
using EEG to examine the biological signals in electrical brain research the
EEG feature extraction algorithm has become a main study content. EEG
acquisition is the use of a 40-Neuroscan amplifier, which is obtained by scan
4.3 software (Ref.) , collected EEG analysis band acquisition using 200Hz low-
pass and high-pass 0.05Hz. Individual experiment is collected continuously for
5 minutes and individual subject every 4 experiments. For 30 subjects, each
subject uses 200 EEG with a total of 6000 EEG. Then, EEG mixed these 30
subjects by using the proposed method of identification for 6000 Brain electric
signals. Each subject identifies the proportion of 70% or more.
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
25
Yanushkevich, Sventlana and Anna Shmerko, 2009 and Mu, Zhendong, 2015
present an artificial immune system inspired approach for identifying users
using EEG signals. The Physionet EEG Motor/Movement/ Imagery dataset is
used to validate this approach ( Schalk, Gerwin et al. 2004 ) ( Goldberger, Ary
et al. 2000 ) . The dataset comprises of signals of over a hundred users. The
dataset is imported to EEG lab for the pre-processing phase, then artificial
immune system When we tried this method it gave very low accuracy so we
changed it to Rough sets.
(Urmila Kalshetti et al.2018 ) EEG signals emitted from a live functions brain are
establish to individual to every human being .These EEG signs would thus be
able to be utilized to shape a biometric framework which beats the deficiencies
of conventional day strategies .the raw EEG signal passes through FIR
Bandpass filter so that the unwanted signal is removed different multiscale
approach , such as Multiscale Wavelet Packet Decomposition and Multiscale
Shape Description are used for feature extraction from the EEG signal. The
sub-bands correspond to delta, theta, alpha, beta and gamma frequency bands
of the brain signals. From the five segments, three are utilized to train Error
Correcting Output Code Support Vector Machine classifier, while the other two
sections are utilized to test on the educated model.
(Electric, Mitsubishi, 2016)This study examine brain waves obtained through
person-grade EEG equipment to explore its capabilities for user identification
and authentication. , the statistical significance of the P300 component in event-
related potential (ERP) data through 14-channel EEGs across 25 subjects.
Used a different of machine learning approach contrasting the client ID
execution of different extraordinary blends of a dimensionality decrease method
pursued by an arrangement calculation. Trial results demonstrate that a
recognizable proof precision of 72% can be accomplished utilizing just a solitary
800 ms ERP age. Also, we exhibit that the client recognizable proof precision
can be altogether moved forward to over 96.7% by joint order of numerous
ages.
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
26
(Maiorana Emanuele et al. 2018) utilized the investigation of the longitudinal
conduct of EEG signals, This paper is the primary broad endeavor, as far as
utilized elicitation conventions, number of included subjects, number of
procurement sessions, and secured time range, to assess the impact of aging
effects on the discriminative abilities of EEG motions over long haul periods. In
particular, we here report and talk about the results acquired from trial tests led
on a database comprising 45 subjects, the longitudinal conduct of EEG
discriminative attributes is assessed by methods for a measurable and
execution related investigation, utilizing diverse EEG includes and shrouded
Markov models as classifiers.
(Abo-Zahhad, M. et al.2015 ) studying another securing convention is embraced
for distinguishing people from electroencephalogram signals dependent on eye
flickering waveforms. For this reason, a database of 10 subjects is gathered
utilizing Neurosky Mind wave headset. Two calculations are actualized for auto-
backward displaying in particular called Levinson-Durbin and Burg algorithms.
Linear and quadratic discriminant functions are tested and compared Using
Burg algorithm with linear discriminant analysis, the proposed system can
identify .
(Kalifa, W. et al. 2015) used an artificial immune system inspired Technique for
identify human by using EEG signals.in this study , The Physionet EEG
Motor/Movement dataset is Utilized to validate this technique . Used a Band
pass filter to remove noise , the data set contain different type of signals such
as Electrooculography(EOG) and Electromyography thus used Blind Source
Separation Algorithm (BSS).
(Feng Lin et al.2018) represented another psycho physiological convention by
means of non-volitional mind reaction for reliable portable confirmation, with an
application model of keen headwear. Especially address the accompanying
exploration challenges in portable biometrics with a hypothetical and
observational consolidated way: (1) how to produce dependable cerebrum
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
27
reactions with advanced visual upgrades, (2) how to obtain the unmistakable
mind reaction and examine extraordinary highlights in the versatile stage, (3)
how to reset and change cerebrum biometrics when the current biometric
accreditation is disclosed. To assess the proposed arrangement directed a pilot
examine and accomplished a f - score exactness of 95.46%, what's more, break
even with blunder rate (EER) of 2.503%, in this manner exhibiting the potential
achievability of neurofeedback based biometrics for intelligent headwear.
Through our study of previous studies, the results are totally promising with
better of 95% accuracy. as shown in Table 1.
Table 1. Feature Extraction with different classification methods
AccuracyMachine
Learning
Technique
SubjectPreprocessing
& Feature
Extraction
year Author No
97% KNN 18 Hadamard
Transform (HT)
2015 Camara, Peris-
lopez, and
Tapiador
1
88% Convolution
Neural
Network
(CNN)
10 Statistical
computation
2015 Lan Ma et al 2
70% - 30 Rough Set 2015 Zhendong Mu 3
- Artificial
immune
system
100 Rough Set 215 Yanushkevich
S. N 4
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
28
- Support
Vector
Machine
- Multiscale
Wavelet Packet
Decomposition
and Multiscale
Shape
Description
2018 Urmila Kalshetti
et al 5
96.7% DT,k-
NN,SVM,
25 Reduced
Dimensionality
2016 Koike-Akino, T.
et al 6
- Hidden
Markov
models
(HMM),
Gaussian
mixture
models
(GMM)
45 AR modeling,
MFCC modeling,
Bump modeling
2018 Emanuele
Maiorana 7
99.8% 10 Levinson-Durbin
and Burg
algorithms
2015 Abo-Zahhad, M.
et al
8
AIS 3 gene encoding 2015 WaelKhalifaet al 9
59.46% SVM - Statistical feature2018 Feng Lin et al 10
International Journal "Information Content and Processing", Volume 6, Number 1, © 2019
29
Conclusion
EEG bio signal approach is the most widely smart method in many security
tasks and dolmans because it is a direct, inexpensive and the simplest method
to acquire brain signals. When using The EEG signal for authentication this
leads to without the need to remember login ID, password, user name and
family name. This paper presents a comprehensive analysis of the recent
literatures on EEG-based biometric person recognition systems and techniques.
The literature has been focused on establishing the presence of biometric data
in EEG signals, when use of EEG biometrics in real-world application scenario
would need much more research to address the shortcoming of the work done
to date especially with regards to the quantity and nature of information
available for system evaluations. In this respect, we need to determine the
efficient computational intelligence and machine learning techniques and
approaches to perform each of the following tasks; signal acquisition,
preprocessing, feature extraction, and classification. Through our previous
study a number of techniques were promising results with a accuracy up to
95%. We hope that this review study will be a useful guidance and largely
facilitate the relevant research in the biometric human identification and
recognition systems.
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Authors' Information
Shaymaa Adnan Abdulrahman- PHD, candidate ,
E-mail: [email protected]
Wael Khalifa – Lecturer at faculty of computer and information sciences, Ain Shams University, Abbasia, Cairo, Egypt,
e-mail: [email protected]
Major Fields of Scientific Research: Medical Informatics, Artificial Intelligence, machine learning, biometrics
Mohamed Roushdy – Professor of Computer Science and Former Dean of Faculty of Computer and Information Sciences, Ain Shams University;
e-mail: [email protected]
Major Fields of Scientific Research: Artificial Intelligence, Medical Informatics
Abdel-Badeeh M. Salem – Professor at faculty of computer and information sciences, Ain Shams University, Abbasia, Cairo, Egypt,
e-mail: [email protected]
Major Fields of Scientific Research: Artificial intelligence(AI), Computational intelligence, Machine learning, Knowledge engineering, Big data analytics, Intelligent biomedical informatics and Healthcare systems, and AI education