A SURVEY OF BIOMETRICS USING ...Shaymaa Adnan Abdulrahman, Wael Khalifa, Mohamed Roushdy,...

International Journal "Information Content and Processing", Volume 6, Number 1, © 2019

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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


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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.


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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


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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).


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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


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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


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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.


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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.


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(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


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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


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- 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


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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;


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,


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

A SURVEY OF BIOMETRICS USING ...Shaymaa Adnan Abdulrahman, Wael Khalifa, Mohamed Roushdy,...

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