Systems, Man & Cybernetics - October 2017 - 8

biometric characteristics for achieving better security and
safety in both personal and technical data communication
and control.
To address the deficiencies of existing biometric
methods or to complement them, there ha s been
increased interest in deploying brain activity for nextgeneration biometric systems [7], [8]. Brain signals' privacy compliance, robustness against spoofing attacks,
and inherent ease regarding detection make them particularly attractive to biometry. Although a few isolated
attempts to identify individuals based on their brain
activity were reported three decades ago [9], systematic
investigation of its use as a distinctive biological trait
has only been initiated during the last decade [10].
Brain activity, inherently determined by a person's
unique pattern of neural pathways, can provide relevant subject-specific information about the brain's anatomical and functional traits [11]. Because brain activation
can be influenced by mood, stress, and the general mental state of an individual, it is very difficult to obtain
under force or threat. Furthermore, brain waves are
closely associated with each individual's unique memory and knowledge base and are therefore impossible to
imitate [12], [13].
As mentioned previously, EEG is one of the most popular techniques for the noninvasive recording of electrical
activity in the brain because activity can be obtained with
the use of portable, comfortable, and relatively inexpensive devices [14]-[16]. To be accepted by an intended

population, a practical EEG-based biometric system (EEGBS)
must be highly universal, unique, permanent, collectable,
acceptable, and less vulnerable to circumvention so that it
is sufficiently robust against various fraudulent methods
and attacks [17], [18]. Although EEG is capable of achieving
moderate-to-high performance in all of these aforementioned measures, acceptance and collectability are the two
fields wherein EEG-based biometrics has limited performance. This is because the measurement of EEG signals
has not reached the required levels of flexibility and comfort in comparison to popular methods used with facial or
fingerprint recognition. Hence, there are challenges in
making an EEGBS a reality even though EEG has many
special merits that deserve to be considered as an ideal
biometric trait. This article outlines recent advances in the
field of EEGBS and discusses the main challenges for its
practical implementation.
Biometric System: Basic Framework
Any biometric system basically operates in three modes [11]:
1)	 enrollment
2)	 authentication
3)	 identification.
Figure 1 shows these modes separately, and each is ex--
plained next.
Enrollment Mode
This mode enrolls all subjects in a database and consists
of three major stages: data acquisition, preprocessing,

Preprocessing

Feature
Extraction

Matching
(1-Many)

Identified
Subject

Identification

Enrollment

Preprocessing

Feature
Extraction

Model
Database

Preprocessing

Feature
Extraction

Matching
(1-1)

True/
False

Verification

Claimed Identity

Figure 1. The general framework of an EEGBS, showing the enrollment, authentication, and identification modes.
8	

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Oc tob e r 2017



Table of Contents for the Digital Edition of Systems, Man & Cybernetics - October 2017

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