Systems, Man & Cybernetics - October 2017 - 12

protocols such as motor imagery,
movements, heartbeat, and muscle
imagined speech, and mental passactivity can also affect EEG. Due to
Existing protocols
the volume conduction of brain
word generation have not yet re--
activity over the scalp, the signalceived the attention necessary
may not constitute
to-noise ratio (SNR) of scalp EEG
from the biometric scientific comthe optimal set of
recordings is low. Hence, proper
munity [18]. In addition, EEGBS
stimuli for eliciting
care must be taken to eliminate all
integrated with responses to audio
kinds of artifacts, retaining activitystimuli is hardly found in the literabrain responses
related information thoroughly.
ture [10]. Online testing is needed
equally and
Despite the low SNR of EEG sigto predict each and every possible
nals, the literature proves the great
hurdle in the practical implementaeffectively for
potential of EEG as a biometric sigtion of EEGBS. For any chosen
every person.
nature [42]. A few existing studies
protocol, the standard biometric
attempt to decipher the issues facproperties of universality, distincing its practical implementation
tiveness, permanence, collectabiliusing low-cost hardware devices. But, unfortunately, most
ty, and uniqueness must be studied in depth using larger
are still in the laboratory phase as a result of a number of
databases [44].
practical challenges [11].
Interoperability of Sensors
Development of Experimental Operations
Electrodes/sensors, within which electrical activity of the
brain is captured, have a significant role in EEGBS. The
interoperability of these recording sensors is an important
Data Acquisition Methods and Collectability
factor in the real-time implementation and generalization
The major obstacle against the deployment of a practical
of EEGBS. The report in [56] emphasizes brain research,
EEGBS is the inconvenience in the data collection procedevelopment, and commercialization techniques for data
dure, which traditionally involves the preparation of EEG
interoperability, which can facilitate brain/technology
electrodes, their placement on the scalp according to the
developments for consumer-friendly applications in the
standard guidelines, and the possible requirement of using
Internet environment. Sensor interoperability ultimately
gel to improve skin conductivity. As a result of the advances
indicates the compatibility of registered biotemplates
in technology, less expensive wireless EEG devices mountwith a new EEG feature vector recorded using a different
ed with dry electrodes are currently available. The EMOTIV
set/type of sensors. Because biometric applications must
Epoc neuroheadset is one popular example [43]. Using this
retain and reuse collected EEG results or features for a
device, it is possible to integrate the equipment with mobiles,
long period of time, sensors may have to interoperate in
microcontrollers, and field-programmable gate arrays as
due course of time for many reasons. Changes in the senprocessing units to build practical EEG biometric authentisor design, resolution of sensors, and recording interface
cation systems. Although the equipment offers more convecan substantially affect the biometric performance, so
nience and comfort than conventional medical devices, its
sensor interoperability must be tested before the practical
quality and stability are yet to be tested.
deployment of EEGBS can take place. Commercialization
For any EEGBS, it is always desirable to have fewer
advancement in brain-computer-interface headsets can
channels for simplifying signal acquisition and further sighelp to standardize EEG signal collection to form an open
nal processing/classification procedures. However, there
databank, allowing data exchange via the Internet. It will
is a lack of comprehensive study in the literature to comalso facilitate the research on interoperable sensors.
pare and optimize all of these protocols and recording
parameters. Thus, further investigation is necessary to
determine the optimum set of EEG channels for providing
Extraction of Subject-Specific Distinct Information
the best accuracy with the lowest complexity in electrode
preparation and procedures. Defining new protocols and
Development of Robust Feature
optimizing the acquisition session duration must be invesExtraction and Matching Algorithms
tigated in future studies.
Subject-specific EEG features are the key elements in the
success of any EEGBS. Robust and distinct features must be
efficiently extracted from sensors using proper artifact
Choice of Operating Protocols
removal, filtering, and signal processing techniques. It is
Although REO, REC, EEG, VEPs, and mental tasks have
essential to classify extracted subject-specific neural traits
been evaluated in the literature for authentication or idenusing robust machine-learning algorithms to implement hightification purposes, only the resting strategy has been
security biometric systems. The extraction of EEG features
explored extensively. Existing protocols may not constican be conducted in both the time and frequency domains
tute the optimal set of stimuli for eliciting brain responses
using various techniques, i.e., AR modeling, PSD, the energy
equally and effectively for every person. In addition,
12	

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



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