# In-ear EEG biometrics for feasible and readily collectable real-world person
authentication

Takashi Nakamura, Valentin Goverdovsky and Danilo P. Mandic Takashi Nakamura,
Valentin Goverdovsky and Danilo P. Mandic are with Department of Electrical
and Electronic Engineering, Imperial College London, London, SW7 2AZ, United
Kingdom, {takashi.nakamura14, goverdovsky<EMAIL_ADDRESS>
###### Abstract

The use of EEG as a biometrics modality has been investigated for about a
decade, however its feasibility in real-world applications is not yet
conclusively established, mainly due to the issues with collectability and
reproducibility. To this end, we propose a readily deployable EEG biometrics
system based on a ‘one-fits-all’ viscoelastic generic in-ear EEG sensor
(collectability), which does not require skilled assistance or cumbersome
preparation. Unlike most existing studies, we consider data recorded over
multiple recording days and for multiple subjects (reproducibility) while, for
rigour, the training and test segments are not taken from the same recording
days. A robust approach is considered based on the resting state with eyes
closed paradigm, the use of both parametric (autoregressive model) and non-
parametric (spectral) features, and supported by simple and fast cosine
distance, linear discriminant analysis and support vector machine classifiers.
Both the verification and identification forensics scenarios are considered
and the achieved results are on par with the studies based on impractical on-
scalp recordings. Comprehensive analysis over a number of subjects, setups,
and analysis features demonstrates the feasibility of the proposed ear-EEG
biometrics, and its potential in resolving the critical collectability,
robustness, and reproducibility issues associated with current EEG biometrics.

##  I Introduction

Person authentication refers to the process of confirming the claimed identity
of an individual, and is already present in many aspects of life, such as
electronic banking and border control. The existing authentication strategies
can be categorised into: 1) knowledge-based (password, PIN), 2) token-based
(passport, card), 3) biometric (fingerprints, iris) [1]. Most extensively used
recognition methods are based on knowledge and tokens, however, these are also
most vulnerable to fraud, such as theft and forgery, and can be
straightforwardly used by imposters. In contrast, biometric recognition
methods rest upon unique physiological or behavioural characteristics of a
person, which then serve as ‘biomarkers’ of an individual, and thus largely
overcome the above vulnerabilities. However, at present, biometric
authentication systems are cumbersome to administer and require considerable
computational and man-power overloads, such as special recording devices and
the corresponding classification software.

With the current issues in global security, we have witnessed a rapid growth
in biometrics applications based on various modalities, which include palm
patterns with high spectral wave [2], patterns of eye movement [3], patterns
in the electrocardiogram (ECG) [4], and otoacoustic emissions [5]. Each such
biometric modality has its strengths and weaknesses, and typically suits only
a chosen type of application and its corresponding scenarios [6]. A robust
biometric system in the real-world should satisfy the following requirements
[1]:

  * •

_Universality_ : each person should possess the given biometric
characteristic,

  * •

_Uniqueness_ : not any two people should share the given characteristic,

  * •

_Permanence_ : the biometric characteristic should neither change with time
nor be alterable,

  * •

_Collectability_ : the characteristic should be readily measurable by a sensor
and readily quantifiable.

In addition, a practical biometric system must be harmless to the users, and
should maximise the trade-off between performance, acceptability, and
circumvention; in other words, it should be designed with the accuracy, speed,
and resource requirements in mind [6].

One of the currently investigated biometric modalities is the
electroencephalogram (EEG), an electrical potential between specific locations
on the scalp which arises as a result of the electrical field generated by
assemblies of cortical neurons, and reflects brain activity of an individual,
such as intent [7]. From a biometrics perspective, the EEG fulfils the above
requirements of _universality_ , as it can be recorded from anyone, together
with the _uniqueness_.  Specifically, the individual differences of EEG alpha
rhythms has been examined [8] and reported to exhibit a significant power in
discriminating individuals [9] in the area of clinical neurophysiology. The
brain activity is neither exposed to surroundings nor possible to be captured
at a distance, therefore the brain patterns of an individual are robust to
forgery, unlike face, iris, and fingerprints. The EEG is therefore more robust
against imposters’ attacks than other biometrics and among different
technologies to monitor brain function. However, in order to utilise EEG
signals in the real-world, several key properties such as _permanence_ and
_collectability_ must be further addressed.

The ‘proof-of-concept’ for EEG biometrics, was introduced in our own previous
works in [10, 11], and most of the follow-up studies were conducted over only
one day (or even over one single trial) recording with EEG channels covering
the entire head, while in the classification stage, the training and
validation datasets were randomly selected from the same recording day (or the
same trial). Apart from its usefulness as a proof-of-concept, this setup does
not satisfy the feasibility requirement for a real-world biometric
application, since:

  * •

Recording scalp-EEG with multiple electrodes is time-consuming to set-up and
cumbersome to wear. Such a sensor therefore does not meet _collectablity_
requirement.

  * •

EEG recordings from one day (or a single trial) cannot truly evaluate the
performance in identifying features of an individual, as this scenario does
not satisfy the _permanence_ requirement either, see details in Section II-B.

  * •

The training and validation data within this scenario are inevitably mixed,
thereby introducing a _performance bias_ in classification. The classification
results from such studies are therefore unrealistically high, and we shall
refer to this setting as the _biased scenario_.

Therefore, for feasible EEG biometrics, the EEG sensor should be wearable,
easy to administer, and fast to set-up, while in order to evaluate the
performance, the recorded signal should be split in a rigorous way – the
training and validation datasets in the classification stage should be created
so as _not to share the same recording days_ , a setting we refer to as the
_rigorous scenario_. While a considerable body of research has been undertaken
to explore the EEG biometrics and to find the most informative subject-
specific characteristic of EEG (_uniqueness_), most studies either focused on
reducing the number of electrodes (_collectability_) or on evaluating whether
the traits are temporally robust (_permanence_) by using EEG data obtained
over multiple recording days; for more details see Section II-C.

In this paper, based on our works in [11] and [12], we bring EEG-based
biometrics into the real-world by resolving the following critical issues:

  1. 1.

_Collectability_. Biometrics verification is evaluated with a wearable and
easy to set-up in-ear sensor, the so-called ear-EEG [12],

  2. 2.

_Uniqueness_ and _permanence_. These issues are addressed through subject-
dependent EEG features which are recorded over temporally distinct recording
days,

  3. 3.

_Reproducibility_. The recorded data are split into the training and
validation data in two different setups, _biased_ and _rigorous_ setup,

  4. 4.

_Fast response_. The classification is performed by both a fast non-parametric
(cosine distance) and standard parametric approaches (linear discriminant
analysis and support vector machine).

Through these distinctive features, we successfully introduce a proof-of-
concept for a wearable in-ear EEG biometrics in the community.

Figure 1: Person recognition systems. _Top_ : Verification system. _Bottom_ :
Identification system.

##  II Overview of EEG based biometrics

###  II-A Biometric systems with verification/identification

Depending on the context, the two categories of biometric systems are: 1)
verification systems and 2) identification systems, summarised in Figure 1
[6]. Verification refers to validating a person’s identity based on their
individual characteristics, which are stored/registered on a server. In
technical terms, this type of a biometric system performs a _one-to-one
matching_ between the ‘claimed’ and ‘registered’ data, in order to determine
whether the claim is true. In other words, the question asked for this
application is ‘Is this person A?’ as illustrated in Figure 1 (top panel). In
contrast, an identification system confirms the identify of an individual from
cross-pattern matching of the all available information, that is, based on
_one-to-many template matching_. The underlining question for this application
is ‘Who is this person?’ as illustrated in Figure 1 (bottom panel).

###  II-B Feasible EEG biometrics design

Traditionally, EEG-based biometrics research has been undertaken based on both
publicly available datasets [11] and custom recordings as part of research
efforts [13]. However, most of existing studies failed to rigorously address
the key criterion, _collectability_ , which is also related to repeatability.
A large number of studies, especially those conducted at the dawn of EEG
biometrics research, employed classification of the clients based on
supervised learning with the training and validation data coming from the same
recording trial. However, this experimental setup cannot truly evaluate the
performance in identifying individual features, since such classification does
not take into account the varying characteristic among multiple recording
trials and recording days. In addition, EEG is prone to contamination by
artefacts from subjects’ movements (e.g. eye blinks, chewing), while the
sources of external noise include electrode noise, power line noise, and
electromagnetic interference from the surroundings. This opens the possibility
to additionally incorrectly associate ‘EEG patterns’ with either trial-
dependent features or so-called noise-related features – in other words, this
setup is _biased_ in favour of high classification rate. Therefore, given the
notorious variability of EEG patterns across days, biometrics studies based on
a single recording day (even for a single subject) can only validate very
limited scenarios, without any notion of repeatability and long-term
feasibility [13].

Figure 2 shows the concept of a _rigorous_ EEG biometrics
verification/identification system in the real-world. Individuals participate
in EEG recordings and their EEG signals are registered and stored on a server
or in a database (left panel). The client is granted access to their account
by providing new EEG data in verification scenarios, whereby the algorithm
discriminates the identify of an individual is in identification scenarios
through new EEG recordings. Recall that the registered EEG must be recorded
beforehand.

In order to fulfil the feasibility requirement, several studies performed
successful EEG-based biometrics from multiple recording trials conducted on
multiple distinct days, thus satisfying the _collectability_ requirement.
However, the majority of these studies were still conducted in an unrealistic
scenario, whereby the training and validation data in the classification
process are split into segments, with all the segments coming from multiple
trials but on the same recording day being randomly assigned to the training
and validation datasets. Therefore, this _biased_ setup, despite being based
on the classification from multiple recording trials, mixes the training and
test recordings from the same recording day and thus cannot truly evaluate the
performance in the identification of individual features.

In order to truly validate the robustness of a EEG biometrics application,
within a _rigorous_ setup, it is therefore necessary to both: i) conduct
multiple recordings over multiple days, and ii) to assign recordings on one
day as the training data and use the recordings from the other days as the
validation data. In other words, the training and validation datasets should
be created _so as not to share the same recording days_ (as illustrated later
in Figure 6, Setup-R). As emphasised by Rocca et al., the issue of the
repeatability of EEG biometrics in different recording sessions is still a
critical open issue [14].

Figure 2: Feasible EEG biometrics verification framework. _Left_ : EEG recording registration. The registered EEG signal must have been recorded beforehand. _Right_ : Verification and identification system. TABLE I: Recording and classification set-up, and performance comparison among existing EEG biometrics Setup |  Author |  Subjects |  Days |  Interval | Ch. | Task∗ |  Segment | Feature Extraction | Classifier∗∗ | Performance
---|---|---|---|---|---|---|---|---|---|---
_Biased_ |  [15] |  10 |  5 |  2 weeks | 4 | EC |  5 s | AR | ANN | CRR = 97.0 %
|  |  |  |  | EO |  | CRR = 96.0 %
2-11 |  [16] |  51 |  4 |  34 $\pm$ 74 days | 2 | EC |  4 s | AR, PSD, MuI, COH, CC | FDA | EER = 3.4 %
2-11 |  [17] |  40 |  2 |  - | 1 | EC |  180 s | AR, PSD | KNN, LDA | CRR = 97.5 %
_Rigorous_ |  [18] |  9 |  3 |  3 days | 8 | MI |  1 s | PSD | MAP model | HTER = 19.3 %
2-11 |  [19] |  4 |  2 |  10 days - 5 months | 1 | EC |  50 s | PSD | LDA | AC = 100 %
2-11 |  [20] |  9 |  2 |  1 - 3 weeks | 3 | EC |  1 s | AR | Linear classier | CRR = 100 %
|  |  |  |  | 5 |  | CRR = 100 %
2-11 |  [21] |  15 |  2 |  5 - 40 days | 1 | ERP |  1.1 s | Time-series | CC | CRR = 89.0 %
|  |  9 |  3 |  134 - 188 days |  | CRR = 93.0 %
|  [22] |  50 |  3 |  Ave. 34 days | 19 | EC |  45 s | AR, PSD, COH | L1, L2, Cos. dist. | R1IR = 90.8 %
|  |  |  |  | EO |  | R1IR = 85.6 %
Task∗ | EC: resting state with eyes closed, EO: resting state with eyes open, MI: motor imagery, ERP: event related potential
Classifier∗∗ | ANN: artificial neural networks, FDA: Fisher discriminant analysis, KNN: k-nearest neighbours, LDA: linear discriminant analysis
|  | MAP: maximum a posteriori, CC: cross correlation, L1 (Manhattan) distance, L2 (Euclidean) distance, cosine distance

###  II-C Previous protocols

Table I summarises the state-of-the-art of the existing EEG biometrics
applications based on multiple data acquisition days.

_Biased setup._ In the first category (Setup: _biased_) is the studies where
the training and validation features were randomly selected regardless of the
data acquisition days. Abdullah et al. [15] collected 4 channels of EEG data
from 10 male subjects during the resting state, in the both eyes open (EO) and
eyes closed (EC) scenarios, in 5 separate recording days over a course of 2
weeks. In each recording day, 5 trials of 30 s recordings were recorded, and
the recorded data were split into 5 s segments with an overlap of 50 %. The
autoregressive (AR) coefficients of the order $p=21$ were extracted from each
segment, and the extracted features were _randomly_ divided into the training
(90 %) and validation (10 %) sets, namely 10-fold cross-validation. An
artificial neural network (ANN) yielded 97.0 % of correct recognition rate
(CRR) for the EC task, and 96.0 % of CRR for the EO task. Riera et al. [16]
recorded 2 forehead channels of EEG from 51 subjects over 4 separate recording
days. The average temporal distance between the 1st and the 4th recording was
34 $\pm$ 74 days. The participants performed the EC task, and the duration of
recordings was between 2 and 4 minutes. The recorded EEG was split into 4 s
segments, and five types of features were calculated for each segment, namely
AR coefficients of order $p=100$, power spectral density (PSD) in the $1-40$
Hz band, mutual information (MuI), coherence (COH), and cross-correlation
(CC). The authors trained various classifiers and identified the 5 best
classifiers. The Fisher’s discriminant analysis (FDA) was then employed and
was first trained with different types of discriminant functions using the 1st
to 3rd recording trials; then the 4th recording trials were used for testing.
Next, the best 5 classifiers from the training process were utilised for
authentication tests, using the first and the second minutes of recordings
from each trial; therefore, _the training data for the classifiers and test
data for the validation were not disjoint_ (biased setup). The discriminant
analysis with the selected discriminant function achieved 3.4 % of equal error
rate (EER). Su et al. [17] analysed 5 minutes of the EC task from 40 subjects,
with 6 recording trials performed in 2 separate recording days for each
subject. The recorded EEG data from the FP1 channel were split into segments
of multiple lengths. The PSD in the $5-32$ Hz band and AR coefficients of the
order $p=19$ were chosen as features. The extracted features were _randomly_
divided into the training (50 %) and validation (50 %) sets. As a result of
100 iterations, the classifier combining Fisher’s linear discriminant analysis
(LDA) and k-nearest neighbours (KNN) achieved an average CRR = 97.5 %, for a
segment length of 180 s.

_Rigorous setup._ Multiple research groups considered EEG biometrics based on
splitting the training and validation data in a _rigorous_ way, _so as not to
share the data from the same recording days_ to highlight the feasibility of
their system (Setup: _rigorous_). Marcel et al. [18] analysed 8 channels of
EEG from 9 subjects, with 4 recording trials over 3 consecutive days. The 15 s
trials consisted of two different motor imagery (MI) mental tasks, the
imagination of hand movements. The recorded data were split into 1 s segments,
and PSD in the $8-30$ Hz band was calculated for each segment. The Gaussian
mixture model (GMM) was chosen as a classifier, and maximum a posteriori (MAP)
estimation was used for adapting a model for client data. By combining
recordings over two days as training data, the authors achieved 19.3 % of half
total error rate (HTER), which is a performance criterion widely used in
biometrics; for more detail see Section III-G. Lee et al. [19] conducted an
experiment of 300 s in duration from four subjects over two days, based on
single channel of EEG in the EC scenario. The data were segmented into
multiple window sizes, and to extract frequency domain features, PSD was
calculated only for the $\alpha$ band ($8-12$ Hz). Even though the dataset
size was relatively small, with 50 s of segment length, the LDA achieved 100 %
classification accuracy. Rocca et al. [20] recorded two resting state EEGs, in
both the eyes open (EO) and eyes closed (EC) scenarios, from 9 subjects over 2
different recording days, which were spanned 1 to 3 weeks. The recording
length was 60 s, and the recorded data were split into 1 s segments with an
overlap of 50 %. The AR model (Burg algorithm) of the order $p=10$ was
employed for feature extraction, and the training and validation data were
split without mixing the trials from different recording days. The recognition
results, with features from selected 3 or 5 channels of scalp EEG, were
obtained by linear classification based on minimising the mean square error
(MMSE), to achieve CRR = 100 %. Armstrong et al. [21] recorded event related
potentials (ERPs) and constructed two datasets. One dataset included EEG
recorded from 15 subjects in two separate days, with a 5 - 40 inter-day
interval, and the other one contained EEG from 9 subjects obtained in three
separate days, with the average interval between the first and third
recordings of 156 days. The recorded data from the O2 channel were split into
1.1 s long segments, which contained an ERP and started from a 100 ms pre-
stimulus. The cross-correlation (CC) between the training and validation data
was used as a feature for classification, and CRR = 89.0 % was achieved for
validating the 2nd day recordings whereas CRR = 93.0 % for classifying the 3rd
day recordings. Maiorana et al. [22] analysed 19 channels of EEG from 50
subjects during both EC and EO tasks in three different recording days, with
the average interval between the first and the third recording of 34 days.
Each recording trial consisted of 240 s of data, segmented into 5 s windows,
with an overlap of 40 %. Three types of features were extracted, including a
channel-wise AR model (using the Burg algorithm) of the order $p=12$, channel-
wise PSD, and the coherence (COH) between the EEG channels. The L1, L2, and
cosine distances were calculated for the extracted features, and the rank-1
identification rate (R1IR) achieved 90.8 % accuracy in the EC task and 85.6 %
in the EO task.

###  II-D Biometrics based on collectable EEG systems

With a perspective of _collectability_ , a biometrics application with dry EEG
electrodes was recently introduced [23]. While conventional wet EEG headsets
require the application of a conductive gel which is generally time-consuming,
the dry headset with 16 scalp channels took on the average 2 minutes to be
operational. The brain-computer interface based biometrics application with
rapid serial visual presentation paradigm achieved CRR = 100 % with 27 s
window size over all 29 subjects. Although the recordings were performed over
a single recording day per subject, the application with a dry headset was a
step forward towards establishing collectable EEG biometrics in real-world.

In a recent effort to enable collectable EEG, the in-ear sensing technology
[12] was introduced into the research community. The ear-EEG has been proven
to provide on-par signal quality, compared to conventional scalp-EEG, in terms
of steady state responses [12, 24], monitoring sleep stages [25, 26], and also
for monitoring cardiac activity [27, 28]. The advantages of the in-ear EEG
sensing for a potential biometrics application in the real-world are:

  * •

_Unobtrusiveness_ : The latest ‘off-the-shelf’ generic viscoelastic EEG sensor
is made from affordable/consumable standard earplugs [29],

  * •

_Robustness_ : The viscoelastic substrate expands after the insertion, so the
electrodes fit firmly inside the ear canal [27],  where the position of
electrodes remains the same in different recording sessions,

  * •

_User-friendliness_ : The sensor can be applied straightforwardly by the user,
without the need for a trained person.

Therefore, biometrics with ear-EEG offers a high degree of _collectability_ ,
a critical issue in real-world applications.  Previously, even based on this
biased scenarios, in-ear EEG based biometrics application has been proposed in
[30].

###  II-E Problem formulation

We investigate the possibility of biometrics verification with a wearable in-
ear sensor, which is capable of fulfilling the _collectability_ requirement.
The data were recorded over temporally distinct recording days, in order to
additionally highlight the _uniqueness_ and _permanence_ aspects. Although the
changes in EEG rhythms may well depend on the time period of years rather than
days, the alpha band features during the resting state with eyes closed were
reported as the most stable EEG feature over two years [31]. Since EEG alpha
rhythms predominantly originate during wakeful relaxation with eyes closed, we
chose our recording task to be the resting state with eyes closed. This task
was used in multiple previous studies [15, 16, 17, 19, 20, 22]. In order to
design a feasible biometrics application in the real-world, we considered
imposters in two different ways: i) registered subjects in a database, and ii)
subjects not belonging to a database. Previously, Riera et al. [16] also used
a single trial of EEG recording from multiple subjects as ‘intruders’, while
the ‘imposters’ data were EEG recordings available from multiple other
experiments. For rigour, we collected two types of data: 1) based on multiple
recordings from  fifteen subjects over two days, and 2) multiple recordings
from  five subjects, which were only used for imposters’ data. The
classification was performed by both a non-parametric and parametric approach.
The non-parametric classifier, minimum cosine distance, is a simplest way for
evaluating the similarity between the training and validation matrix, whereas
the parametric approach, the support vector machine (SVM), was tuned within
the training matrix in order to find optimal hyper-parameters and weights for
validation. The same hyper-parameters and weights were used for classifying
the validation matrix. Besides, the linear discriminant analysis (LDA) was
also employed as a classifier. Through the binary client-imposter
classification, we then evaluated the feasibility of our in-ear EEG
biometrics.

##  III Methods

###  III-A Data acquisition

The recordings were conducted at Imperial College London, for two different
groups of subjects and under the ethics approval, Joint Research Office at
Imperial College London ICREC12_1_1. One set of data were the recordings used
as both clients and imposters data, denoted by $S_{R}$, and the other subset
were the recordings for only imposters’ data, denoted by $S_{N}$. Table II
summarises the two recording configurations of $S_{R}$ and $S_{N}$.

For the $S_{R}$ subset of recordings,  fifteen healthy male subjects (aged
22-38 years) participated in two temporally separate sessions, with the
interval between two recording sessions between 5 and 15 days, depending on
the subject. The participants were seated in a comfortable chair during the
experiment, and were asked to rest with eyes closed. The length of each
recording was 190 s, and the recording was undertaken three times (trials) per
one day. The interval between each recording trial was approximately 5 to 10
minutes. In total, six trials were recorded per subject. The in-ear sensor was
inserted in the subject’s left ear canal after earwax was removed; it then
expanded to conform to the shape of the ear canal. The reference gold-cup
standard electrodes were attached behind the ipsilateral earlobe and the
ground electrodes were placed on the ipsilateral helix. For simplicity, the
upper electrode is denoted by Ch1, while Ch2 refers to the bottom electrode,
as shown in Figure 3 (left panel). The two EEG signals from flexible
electrodes were recorded using the g.tec g.USBamp amplifier with a 24-bit
resolution, at a sampling frequency $fs$ = 1200 Hz.

For the $S_{N}$ subset of recordings,  five healthy subjects (aged 22-29
years) participated in three recording trials. Similar to the $S_{R}$ subset
of recordings, the participants were seated in a comfortable chair, and were
resting with eyes closed. The duration of recording was also  190 s. A generic
earpiece with two flexible electrodes [29] was inserted in the subject’s  left
ear canal and the same reference and ground configuration was utilised for the
$S_{R}$ subset of recordings.

Similar to the setup in [22], there was no restriction on the activities that
the subjects performed, and no health test such as their diet and sleep, was
carried out neither before or between an EEG acquisition and the following
one, nor during the days of the recordings. This lack of restrictions allowed
us to acquire data in conditions close to real life.

TABLE II: Two EEG recordings and corresponding subset Subset | Client/Imposter $S_{R}$ | Imposter only $S_{N}$
---|---|---
Task | Resting state with eyes closed
No. Subjects | 15 | 5
No. Trials | 6 (2 days, 3 trials) | 3 (1 day, 3 trials)
Duration | 190 s
Figure 3: The in-ear sensor used in our study. _Left_ : Wearable in-ear sensor
with two flexible electrodes. _Right_ : Placement of the generic viscoelastic
earpiece. Figure 4: Flowchart for the biometrics analysis framework in this
study.

###  III-B Ear-EEG sensor

The in-ear EEG sensor is made of a memory-foam substrate and two conductive
flexible electrodes, as shown in Figure 3. The substrate material is a
viscoelastic foam, therefore the ‘one-fits-all’ generic earpiece fits any ear
regardless of the shape. The size of earpiece was the same for over twenty
subjects (both the $S_{R}$ and $S_{N}$ subjects). Further details of the
construction of such a viscoelastic earpiece and its detailed recordings of
various brain functions can be found in [29, 27].

###  III-C Pre-processing

The two channels of the so-obtained ear-EEG were analysed based on the
framework illustrated in Figure 4. In each recording, for both the $S_{R}$ and
$S_{N}$ recordings, the first 5 s of recording data were removed from the
analyses, in order to omit  noisy recordings arising at the beginning of the
acquisition. The two recorded channels of EEG were bandpass filtered with the
fourth-order Butterworth filter with the pass-band $0.5-30$ Hz. The bandpass
filtered signals were split into segments.  The symbol $N$ denotes the number
of segments per recording trial from both the $S_{R}$ and $S_{N}$ subsets. The
lengths of segments were chosen as $L_{seg}=10,20,30,60,90$ s. Therefore, when
the segment length was $L_{seg}=$ 60 s, $N=3$ ($190-5=185$ s, $\left\lfloor
185/60\right\rfloor=3$) segments were extracted from every recording trial of
the $S_{R}$ and $S_{N}$. Within each segment, the data was split into epochs
of 2 s length. The epochs with the amplitudes of greater than 50 $\mu$V for
either Ch1 or Ch2 were considered corrupted by artefacts and removed from the
analyses. This method resulted in a loss of 4.3 % of the data, namely
approximately 7.7 s out of 190 s per recording trial.

###  III-D Feature Extraction

After the pre-processing, two types of features were extracted from each
segment of the ear-EEG. For a fair comparison with the state-of-the-art, these
features were selected to be the same or similar to those used in the recent
studies based on the resting state with eyes closed [19, 20], and included: 1)
a frequency domain feature – power spectral density (PSD), and 2) coefficients
of an autoregressive (AR) model.

####  III-D1 The PSD features

Figure 5 shows power spectral density for the in-ear EEG Ch1 (left) and for
the in-ear EEG Ch2 (right) of two subjects. For this analysis, the recorded
signals were conditioned with the fourth-order Butterworth filter with the
pass-band $0.5-30$ Hz. The PSD were obtained using Welch’s averaged
periodogram method [32], the window length was 20 s with 50 % of overlap. The
PSDs are overlaid between different recording days (red: Day1, blue: Day2), as
well as among different recording trials with the same recording days,
especially visible from 3 to $20$ Hz. Previously, Maiorana et al. utilised PSD
features for EEG biometrics based on the resting state eyes closed and
achieved the best performance between the PSD features from theta to beta
band, which was classified by the minimum cosine approach [22]; the inclusion
of the delta band decreased their identification performance. In our in-ear
EEG biometrics approach, the obtained PSDs were visually examined and we found
that the ratio between the the total $\alpha$ band ($8-13$ Hz) power and the
total $\theta-\alpha_{high}$ band ($4-16$ Hz) power is a relatively more
significant individual factor for biometrics, rather than the total $\alpha$
band ($8-13$ Hz) power, which is proposed in [19]. Therefore, in each segment
of length $L_{seg}$, univariate PSD was calculated by Welch’s method with 2 s
of the window length and no overlap. Three features were obtained for each
PSD: 1) The ratio between the total $\alpha$ band ($8-13$ Hz) power and the
total $\theta-\alpha_{high}$ band ($4-16$ Hz) power, 2) the maximum power in
$\alpha$ band, and 3) the frequency corresponding to the maximum of $\alpha$
band power. In total, $D=6$ (three features $\times$ two channels) frequency
domain features were extracted from each segment.

Figure 5: Power spectral density for the in-ear EEG Ch1 (left) and the in-ear
EEG Ch2 (right) of Subject 1 (top panels) and Subject 2 (bottom panels). The
thick lines correspond to the averaged periodogram obtained by the all
recordings from the 1st day (red) and the 2nd day (blue), whereas the thin
lines are the averaged periodogram obtained by a single trial.

####  III-D2 The AR features

The Burg algorithm [32] of order $p=10$ was used to estimate the AR
coefficients. For each segment, we applied univariate AR parameter estimation
of its $\alpha$ band ($8-13$ Hz) with a window length of 2 s and no overlap.
The AR model was chosen as a feature, because it was used in a previous
successful study on EEG biometrics based on the resting state with eyes closed
[20]. A total of $D=20$ features (ten coefficients $\times$ two channels) were
therefore extracted for each ear-EEG segment.

###  III-E Validation scenarios

With the extraction of the both univariate AR and PSD features from two
channels, the dimension $D$ of features per EEG segment was twenty six. Recall
that the first 5 s of recording data were removed from the analyses. For each
trial of the $S_{R}$ and $S_{N}$ recordings, the data with the duration of 190
s was split into segments of length $L_{seg}=10,20,30,60,90$ s. Therefore,
$N=18,9,6,3,2$ segments were respectively obtained. Each recording trial was
represented by the feature matrix $X_{R}$ for the $S_{R}$ recordings and
$X_{N}$ for the $S_{N}$ recordings, such matrices have $N\times D$ elements.
In this way, a set of six feature matrices was obtained from one subject for
the $S_{R}$ recordings (three recording trials per one day, over two different
days), whilst a set of three feature matrices was obtained from one subject
for the $S_{N}$ recordings (three recording trials per one day, one recording
day). We next discuss the use of feature matrices in two different validation
scenarios.

As emphasised in Introduction, we introduce a feasible EEG biometrics which
satisfies the _collectability_ requirements, which are also related to
repeatability. Therefore, for rigour, we used all feature matrices $X_{R}$
from the 1st day of recordings as the training data, and feature matrices from
the 2nd day of recordings as the validation data, and vice versa
(Setup-R)111Setup-_R_ : _Rigorous_ setup, Setup-_B_ : _Biased_ setup.. Our
goal was to examine the robustness of the proposed approach over the two
different time periods in Setup-R. For the second setup,
Setup-B††footnotemark: , training feature matrices were also selected from the
trials which were recorded at the same recording day as the validation
matrices. Namely, the training and validation data are split by mixing the
data from the same recording days. Notice that, although used in most
available EEG biometrics studies [15, 16, 17], Setup-B could not evaluate the
repeatability/reproducibility of the application, because the training and
validation data were both from the same recording days. In other words, such
an approach benefits from the recording-day-dependent EEG characteristic in
the classification. However, as the number of feasible biometric modalities
with in-ear EEG sensor is limited and for comparison with other studies, for
convenience we also provide the results for Setup-B.

Algorithm 1 , Setup-R (rigorous): Select the training and validation data
without mixing segments from the two recording days, e.g. $[i,j,k]=$ [1,1,1]

1: $VC$: The matrix of the selected-subject, the selected-day and the
selected-trial, [1,1,1] $Y_{VC}=X_{R}^{(1,1,1)}$

2: $VI$: The matrices of the selected-trial and the selected-day from the non-
selected-subjects, [(2:15),1,1]
$Y_{VI}=[X_{R}^{(2,1,1)T},X_{R}^{(3,1,1)T},...,X_{R}^{(15,1,1)T}]^{T}$

3: $TC$: The matrices of all recording trials recorded at the non-selected-day
from the selected-subject, [1,2,(1:3)]
$Y_{TC}=[X_{R}^{(1,2,1)T},X_{R}^{(1,2,2)T},X_{R}^{(1,2,3)T}]^{T}$

4: $TI$: The matrices of all recording data recorded at the non-selected-day
from the non-selected-subjects, [(2:15),2,(1:3)]
$Y_{TI}=[X_{R}^{(2,2,1)T},...,X_{R}^{(2,2,3)T},X_{R}^{(3,2,1)T},...,X_{R}^{(15,2,3)T}]^{T}.$

5: $VI_{N}$: The matrices of the selected-trial from the $S_{N}$ recording
subjects, [(1:5),-,1]
$Y_{VI_{N}}=[X_{N}^{(1,-,1)T},X_{N}^{(2,-,1)T},...,X_{N}^{(5,-,1)T}]^{T}.$

Algorithm 2 , Setup-B (biased): Select the training and validation data with
mixing segments from the two recording days, e.g. $[i,j,k]=$ [2,2,2]

1: $VC$: The matrix of the selected-subject, the selected-day and the
selected-trial, [2,2,2] $Y_{VC}=X_{R}^{(2,2,2)}$

2: $VI$: The matrices of the selected-trial and the selected-day from the non-
selected-subjects, [(1,3:15),2,2]
$Y_{VI}=[X_{R}^{(1,2,2)T},X_{R}^{(3,2,2)T},...,X_{R}^{(15,2,2)T}]^{T}.$

3: $TC$: The matrices of all recording trials recorded at the non-selected day
from the selected-subject, [2,1,(1:3)], and the matrices of non-selected
trials recorded at the selected-day from the selected-subject, [2,2,(1,3)]
$Y_{TC}=[X_{R}^{(2,1,1)T},X_{R}^{(2,1,2)T},X_{R}^{(2,1,3)T},X_{R}^{(2,2,1)T},X_{R}^{(2,2,3)T}]^{T}.$

4: $TI$: The matrices of all recording data recorded at the non-selected-day
from the non-selected-subjects, [(1,3:15),1,(1:3)], and the matrices of non-
selected trials recorded at the selected-day from the non-selected-subjects,
[(1,3:15),2,(1,3)]
$Y_{TI}=[X_{R}^{(1,1,1)T},X_{R}^{(1,1,2)T},X_{R}^{(1,1,3)T},X_{R}^{(1,2,1)T},X_{R}^{(1,2,3)T},X_{R}^{(3,1,1)T},...,X_{R}^{(15,2,3)T}]^{T}.$

5: $VI_{N}$:  The matrices of the selected-trial from the $S_{N}$ recording
subjects, [(1:5),-,2]
$Y_{VI_{N}}=[X_{N}^{(1,-,2)T},X_{N}^{(2,-,2)T},...,X_{N}^{(5,-,2)T}]^{T}.$

Figure 6 summarises the two validation scenarios, Setup-R and Setup-B. For
clarity, we denote by $VC$ the validation feature matrix for the client, by
$VI$ the validation feature matrix for the imposters, while $TC$ is the
training feature matrix for the client, and $TI$ as the training feature
matrix for the imposters. The feature matrix from a single trial of the
subject $i$, recording day $j$, and trial $k$, from the $S_{R}$ recordings is
denoted by $X_{R}^{(i,j,k)}$. Then, the training feature matrix $Y_{T}$ and
the validation matrix $Y_{V}$ are given as

$\displaystyle Y_{T}$ $\displaystyle=$
$\displaystyle[Y_{TC}^{T},Y_{TI}^{T}]^{T},$ $\displaystyle Y_{V}$
$\displaystyle=$ $\displaystyle Y_{V_{R}}=[Y_{VC}^{T},Y_{VI}^{T}]^{T}.$

Besides, in order to evaluate feasibility in the real-world, we used $S_{N}$
recordings, which are EEG recordings only used for imposters; Riera et al.
termed the imposter only data as ‘intruders’ [16]. For an additional scenario
in both Setup-R and Setup-B (see Section IV-D), $S_{N}$ recordings were used
as the validation data for imposters, $VI_{N}$. The feature matrix from a
single trial of the subject $i$, and trial $k$, from the $S_{N}$ recordings is
denoted by $X_{N}^{(i,-,k)}$. Therefore, the validation matrix is given by

$\displaystyle Y_{V}$ $\displaystyle=$ $\displaystyle
Y_{V_{R}}+Y_{VI_{N}}=[Y_{VC}^{T},Y_{VI}^{T},Y^{T}_{VI_{N}}]^{T}.$

Table III summarises the properties of matrices for the training matrix and
the validation matrix in the both Setup-R and Setup-B.

Figure 6: Two validation scenarios (Setup-R and Setup-B), where $X_{R}^{(i,j,k)}\in\mathbb{R}^{N\times D}$ and $X_{N}^{(i,-,k)}\in\mathbb{R}^{N\times D}$ denote a feature matrix from a single trial of the subject $i$, recording day $j$, and trial $k$, from the $S_{R}$ recordings and the $S_{N}$ recordings, respectively. The number of segments per recording trial $N$ depends on the chosen segment lengths $L_{seg}$. The dimension $D$ is the number of features per EEG segment. TABLE III:  Dimensions of the training and validation matrix in Setup-R and Setup-B |  | Setup-R | Setup-B
---|---|---|---
Train |  $Y_{TC}$ | $3N\times D$ | $5N\times D$
|  $Y_{TI}$ | $42N\times D$ | $70N\times D$
2-4 |  $Y_{T}$ | $45N\times D$ | $75N\times D$
Validation |  $Y_{VC}$ | $N\times D$
|  $Y_{VI}$ | $14N\times D$
2-4 |  $Y_{V_{R}}$ | $15N\times D$
|  $Y_{VI_{N}}$ | $5N\times D$
Total validation | $\begin{array}[]{lcl}Y_{V}&=&Y_{V_{R}}:90(15N)\\\ Y_{V}&=&Y_{V_{R}}+Y_{VI_{N}}:90(15N+5N)\end{array}$
elements |  $i=$ 1:15 (Sub), $j=$ 1:2 (Day), $k=$ 1:3 (Trial)

###  III-F Classification

For both the Setup-R and Setup-B, we selected every trial from every subject
for the validation of client data, so as to have validated ninety times (three
trials $\times$ two days $\times$ fifteen subjects). For each validation, both
the largest and smallest values were found for each feature (column-wise) from
the training matrix, then the validation matrix was normalised to the range
$[0,1]$ based on these largest and smallest values. Three classification
algorithms were employed: 1) a non-parametric approach – minimum cosine
distance [22], 2, 3) parametric approaches –  linear discriminant analysis
(LDA) [33] and support vector machine (SVM) [34].

####  III-F1 Cosine distance

The cosine distance is the simplest way for evaluating the similarity between
the rows of the validation matrix,  $Y_{V_{(l,:)}}$, where $l=1,...,15N$ for
$Y_{V}=Y_{V_{R}}$ and $l=1,...,20N$ for $Y_{V}=Y_{V_{R}}+Y_{VI_{N}}$, and the
training matrix, $Y_{T}$, and is given by

$\displaystyle
d\left(Y_{V_{(l,:)}},Y_{T}\right)=\min_{n}\frac{\sum_{m=1}^{D}Y_{V_{(l,m)}}Y_{T_{(n,m)}}}{\sqrt{\sum_{m=1}^{D}(Y_{V_{(l,m)}})^{2}}\sqrt{\sum_{m=1}^{D}(Y_{T_{(n,m)}})^{2}}}.$

In other words, the cosine distance is used for evaluating the similarity
between a given test sample (e.g $l$th row of the validation matrix,
$Y_{V_{(l,:)}}$) and a template (training) feature matrix, $Y_{T}$. The
distances between the $l$th row of the validation matrix, $Y_{V_{(l,:)}}$, and
the each row of training matrix $Y_{T}$ were first computed, then the minimum
among the computed distances was selected.

####  III-F2 LDA

The binary-class LDA was employed as a classifier. The LDA finds a linear
combination of parameters to separate given classes. The LDA projects the data
onto a new space, and discriminates between two classes by maximising the
between-class variance while minimising the within-class variance.

####  III-F3 SVM

The binary-class SVM was employed as a parametric classifier [34]. For both
Setup-R and Setup-B, four hyper-parameters: type of kernel, regularisation
constant for loss function $C$, inverse of bandwidth $\gamma$ of kernel
function, and order of polynomial $d$, were tuned by 5-fold cross-validation
within the training matrix. Then, the same hyper-parameters were used in order
to obtain the optimal weight parameters within the training matrix. The same
hyper-parameters and weight parameters as in the training were used for
validation. Table IV summarises the hyper-parameters for SVM.

TABLE IV: Hyper-parameters for SVM Type of kernel | $\kappa(\mathbf{x},\mathbf{x}^{\prime})$ | Hyper-parameters
---|---|---
Linear | $\mathbf{x}^{T}\mathbf{x}^{\prime}$ | -
Sigmoid | $\tanh(\gamma\mathbf{x}^{T}\mathbf{x}^{\prime}+r)$ | $\gamma,(r=0)$
RBF | $\exp(-\gamma|\mathbf{x}-\mathbf{x}^{\prime}|^{2})$ | $\gamma$
Polynomial | $(\gamma\mathbf{x}^{T}\mathbf{x}+r)^{d}$ | $\gamma,d,(r=0)$

###  III-G Performance evaluation

Feature extraction and classification with minimum cosine distance and with
LDA was performed using Matlab 2016b, and the classification with SVM was
conducted in Python 2.7.12 Anaconda 4.2.0 (x86_64) operated on an iMac with
2.8GHz Intel Core i5, 16GB of RAM.

For the verification setup (the number of classes $M=2$, client-imposter
classification), the performance was evaluated through the false accept rate
(FAR), false reject rate (FRR), half total error rate (HTER), accuracy (AC),
and true positive rate (TPR), defined as:

$\displaystyle FAR=FP/(FP+TN),\,\,FRR=FN/(TP+FN),\,\,\,\,\,\,\,\,$
$\displaystyle HTER=\frac{FAR+FRR}{2},\,\,AC=\frac{TP+TN}{TP+FN+FP+TN},$
$\displaystyle
TPR=TP/(TP+FN).\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,$

The parameter TP (true positive) represents the number of positive (target)
segments correctly predicted, TN (true negative) is the number of negative
(non-target) segments correctly predicted, FP (false positive) is the number
of negative segments incorrectly predicted as the positive class, and FN
(false negative) is the number of positive segments incorrectly predicted as
negative class.

For the identification setup ($M=15$), the performance was evaluated by
subject-wise sensitivity (SE), identification rate (IR) and Cohen’s kappa
($\kappa$) coefficient as:

$\displaystyle
SE_{i}=TP_{i}/(TP_{i}+FN_{i}),\,\,IR=\frac{\sum_{i=1}^{15}TP_{i}}{N_{segment}}$
$\displaystyle\pi_{e}=\frac{\sum_{i=1}^{15}\left\\{(TP_{i}+FP_{i})(TP_{i}+FN_{i})\right\\}}{{N_{segment}}^{2}},\,\,\kappa=\frac{IR-\pi_{e}}{1-\pi_{e}},$

where $N_{segment}$ is the total number of segments.

##  IV Results

The biometric verification results within a one-to-one client-imposter
classification problem are next summarised. In terms of the verification, we
considered the following scenarios:

  * •

Client-imposter verification based on varying segment lengths $L_{seg}$
(Section IV-A),

  * •

Verification with various combinations of features (Section IV-B),

  * •

Verification across different classifiers, both non-parametric and parametric
ones (Section IV-C),

  * •

Verification of registered clients and imposters ($S_{R}$), and of non-
registered-imposters ($S_{N}$) (Section IV-D),

  * •

Subject-wise verification (Section IV-E).

We also considered biometric identification, that is, a one-to-many subject-
to-subject classification problem (Section IV-F). Table V summarises the
details of the considered scenarios.

TABLE V: Summary of parameter choice in the proposed biometric application Section | No. Subject | $L_{seg}$ | Features | Classifier | Setup | System
---|---|---|---|---|---|---
IV-A | 15 | $10,20,30,60,90$ | PSD+AR | Cos. Dist. | R&B | Verification
IV-B | 15 | $60$ | PSD, AR, PSD+AR | Cos. Dist. | R
IV-C | 15 | $60$ | PSD+AR | Cos. Dist., LDA, SVM | R&B
IV-D | 15+5 | $60$ | PSD+AR | Cos. Dist., LDA, SVM | R&B
IV-E | 15 | $60$ | PSD+AR | Cos. Dist. | R
1-7 IV-F | 15 | $10,20,30,60,90$ | PSD+AR | Cos. Dist. | R&B | Identification

###  IV-A Client-Imposter verification with different segment sizes

Table VI summarises validation results for both Setup-R and Setup-B, over
different segment sizes $L_{seg}=10,20,30,60,90$ s. Both the PSD and AR
features were used. The elements in TP, FN, FP and TN columns denote the
number of segments classified by a validation stage. The chance level in this
scenario is 14/15 = 93.3 %; this is because every subject is once used for
client data, $V_{VC}\in\mathbb{R}^{N\times D}$ in Table III, and imposter data
are selected from the other ‘non-client’ subjects,
$V_{VI}\in\mathbb{R}^{14N\times D}$ in Table III. The ratio between $V_{VC}$
and $V_{VI}$ is therefore $1:14$, and thus the chance level is 14/15. In
Setup-R, the results with $L_{seg}=60$ s achieved both the best HTER score,
17.2 %, and the best accuracy (AC), 95.7 %.  Notice that, the number of TP
(=183) is larger than FN + FP (=174) with $L_{seg}=60$ s, therefore, the
likelihood of making a true positive verification is higher than making a
false verification. In Setup-B, the results with $L_{seg}=90$ s obtained both
the best HTER score, 6.9 %, and the best accuracy (AC), 98.3 %.

TABLE VI: Client-Impostor verification over different segment sizes |  $L_{seg}$ |  TP |  FN |  FP |  TN |  FAR |  FRR |  HTER |  AC
---|---|---|---|---|---|---|---|---|---
_Setup-R_ |  10 s |  622 |  996 |  996 |  21656 |  4.4 |  61.6 |  33.0 |  91.8
|  20 s |  371 |  439 |  439 |  10901 |  3.9 |  54.2 |  29.1 |  92.8
|  30 s |  271 |  269 |  269 |  7291 |  3.6 |  49.8 |  26.7 |  93.4
|  60 s |  183 |  87 |  87 |  3693 |  2.3 |  32.2 |  17.2 |  95.7
|  90 s |  120 |  60 |  60 |  2460 |  2.4 |  33.3 |  17.8 |  95.6
_Setup-B_ |  10 s |  1097 |  523 |  523 |  22157 |  2.3 |  32.3 |  17.3 |  95.7
|  20 s |  611 |  199 |  199 |  11141 |  1.8 |  24.6 |  13.2 |  96.7
|  30 s |  425 |  115 |  115 |  7445 |  1.5 |  21.3 |  11.4 |  97.2
|  60 s |  233 |  37 |  37 |  3743 |  1.0 |  13.7 |  7.3 |  98.2
|  90 s |  157 |  23 |  23 |  2497 |  0.9 |  12.8 |  6.9 |  98.3
TP: True Positive, FN: False Negative, FP: False Positive, TN: True
Negative, FAR: False Accept Rate, FRR: False Reject Rate,
HTER: Half Total Error Rate, AC: Accuracy

###  IV-B Client-Imposter verification with different features

Table VII shows the validation results in Setup-R, and over a range of
different selections of features, such as AR coefficients, frequency band
power, and the combination of AR and band power features for the segment
length of $L_{seg}=60$ s. The classification results using both AR features
and PSD features were the highest in terms of both HTER and AC, which
corresponds to Table VI (upper-panel), for $L_{seg}=60$ s.

TABLE VII: Rigorous setup: Client-Imposter verification over different features in Setup-R $L_{seg}=60$ | No. feature $D$ | FAR | FRR | HTER | AC
---|---|---|---|---|---
AR | 20 | 4.8 | 66.7 | 35.8 | 91.1
PSD | 6 | 4.4 | 61.1 | 32.8 | 91.9
AR + PSD | 26 | 2.3 | 32.2 | 17.2 | 95.7

###  IV-C Client-Imposter verification with different classifiers

Table VIII shows the imposter-client verification accuracy based on the
minimum cosine distance, LDA, and SVM, for both Setup-R and Setup-B, with a
segment size of $L_{seg}=60$ s. Both the PSD and AR features were used. In
Setup-R, the results with cosine distance were the best in terms of both HTER
score, 17.2 % and AC, 95.7 %. In Setup-B, the results of both HTER and AC were
the best based on the SVM classifier, 5.5 % and 99.0 %, respectively.

TABLE VIII: Client-Impostor verification with different classifiers $L_{seg}=60$ s |  TP |  FN |  FP |  TN |  FAR |  FRR |  HTER |  AC
---|---|---|---|---|---|---|---|---
_Setup-R: rigorous_
Cos. Dist |  183 |  87 |  87 |  3693 |  2.3 |  32.2 |  17.2 |  95.7
LDA |  149 |  121 |  139 |  3641 |  3.7 |  44.8 |  24.2 |  93.6
SVM |  135 |  135 |  54 |  3726 |  1.4 |  50.0 |  25.7 |  95.3
_Setup-B: biased_
Cos. Dist |  233 |  37 |  37 |  3743 |  1.0 |  13.7 |  7.3 |  98.2
LDA |  200 |  70 |  87 |  3693 |  2.3 |  25.9 |  14.1 |  96.1
SVM |  241 |  29 |  11 |  3769 |  0.3 |  10.7 |  5.5 |  99.0

###  IV-D Validation including non-registered imposters

Table IX summarises the confusion matrices of both Setup-R and Setup-B with
segment sizes $L_{seg}=60$ s, classified by the minimum cosine distance, LDA,
and SVM; these correspond to Table VIII, panels Setup-R and Setup-B. The
confusion matrices were categorised into:

  * •

Client matrix $Y_{VC}$ from dataset $S_{R}$,

  * •

Imposter matrix $Y_{VI}$ from dataset $S_{R}$

  * •

Imposter matrix $Y_{VI_{N}}$ from dataset $S_{N}$.

Notice that the minimum cosine distance approach assigns the class (client or
imposter) of the nearest data from the training matrix. In this study, we
selected every trial from every subject for the validation of client data, so
as to have validated ninety times; therefore, _the nearest data (from dataset
$S_{R}$) for each imposter data from dataset $S_{N}$, also always become the
‘client’ once._ Hence, when the nearest data for an $S_{N}$ data become the
‘client’ data in the training matrix, the imposter data from dataset $S_{N}$
are straightforwardly classified as ‘client’. Therefore, regardless of data,
the TPR for Imposter matrix $Y_{VI_{N}}$ is 93.3 % for the minimum cosine
distance approach; however for comparison among different classifiers, these
results are also included.

In Setup-R, the TPR of client $Y_{VC}$, achieved by the minimum cosine
distance was the highest, with respective value of 67.8 %. However, the TPRs
obtained by SVM for imposters $Y_{VI}$ and $Y_{VI_{N}}$ were 98.6 % and 96.2
%, respectively, which was higher than those achieved by LDA. In Setup-B, both
the TPR of client $Y_{VC}$ and that of imposters $Y_{VI}$ and $Y_{VI_{N}}$ by
SVM were the highest, with respective values of 89.3 %, 99.7 % and 96.3 %.

TABLE IX: Confusion matrix of the Client-Imposter verification scenario from different datasets in both Setup-R and Setup-B $L_{seg}=60$ s | Prediction |  |  TPR
---|---|---|---
|  |  |  Client |  Imposter |  Total |  (%)
_Setup-R_ Label |  Cos. |  Client $Y_{VC}$ |  183 |  87 |  270 |  67.8
|  |  Imposter $Y_{VI}$ |  87 |  3693 |  3780 |  97.7
|  |  Imposter $Y_{VI_{N}}$∗ |  90∗ |  1260∗ |  1350∗ |  93.3∗
2-7 |  LDA |  Client $Y_{VC}$ |  149 |  121 |  270 |  55.2
|  |  Imposter $Y_{VI}$ |  139 |  3641 |  3780 |  96.3
|  |  Imposter $Y_{VI_{N}}$ |  136 |  1214 |  1350 |  89.9
2-7 |  SVM |  Client $Y_{VC}$ |  135 |  135 |  270 |  50.0
|  |  Imposter $Y_{VI}$ |  54 |  3726 |  3780 |  98.6
|  |  Imposter $Y_{VI_{N}}$ |  52 |  1298 |  1350 |  96.2
_Setup-B_ Label |  Cos. |  Client $Y_{VC}$ |  233 |  37 |  270 |  86.3
|  |  Imposter $Y_{VI}$ |  37 |  3743 |  3780 |  99.0
|  |  Imposter $Y_{VI_{N}}$∗ |  90∗ |  1260∗ |  1350∗ |  93.3∗
2-7 |  LDA |  Client $Y_{VC}$ |  200 |  70 |  270 |  74.1
|  |  Imposter $Y_{VI}$ |  87 |  3693 |  3780 |  97.7
|  |  Imposter $Y_{VI_{N}}$ |  152 |  1198 |  1350 |  88.7
2-7 |  SVM |  Client $Y_{VC}$ |  241 |  29 |  270 |  89.3
|  |  Imposter $Y_{VI}$ |  11 |  3769 |  3780 |  99.7
|  |  Imposter $Y_{VI_{N}}$ |  50 |  1300 |  1350 |  96.3
∗ Always the same result regardless of the data

###  IV-E Client-Imposter verification results per subject

Table X (middle columns) summarises the subject- and day-wise validation
results with PSD and AR features from $L_{seg}=$ 60 s segments in Setup-R,
which corresponds to Table VI (upper-panel) for $L_{seg}=$ 60 s. The first and
second columns in the Verification part show respectively subject-wise HTER
and AC, with the training matrix $Y_{T}$ selected from all the first day
recordings and classified based on the second day recordings. The third and
fourth columns in the Verification part show classification results obtained
based on the training matrix $Y_{T}$, which was selected from all the second
day recordings in order to classify the first day recordings.

###  IV-F Biometrics identification scenarios

Table X (right column) summarises the subject-wise identification rate
obtained by the minimum cosine distance classifier with the PSD and AR
features from $L_{seg}=60$ s segments in Setup-R. Previously, we considered a
binary client-imposter classification problem (e.g. $M=2$) for each subject,
each day, and each trial, however, the classification algorithm used in this
study was the simple minimum cosine distance between the validation matrix and
training matrix. For the prediction of $l$th row of the validation matrix,
$Y_{V_{(l,:)}}$, the minimum distance between the training matrix, $Y_{T}$,
was found, e.g. the $n$th row of the training matrix, and the same label was
assigned to the $n$th row of the training matrix as the prediction label for
$l$th row of the validation matrix, $Y_{V_{(l,:)}}$. Notice that _the minimum
distance approach is applicable for biometrics identification problems, which
is a one-to-many classification._ The number of classes was $M=15$, which
corresponds to the the number of subjects in the $S_{R}$ recordings, therefore
the chance level was 1/15 = 6.7 %. The achieved identification rate was 67.8 %
with $L_{seg}=60$ s segments in Setup-R, while the achieved Cohen’s kappa
coefficient was $\kappa=0.65$ (Substantial agreement) [35].

Figure 7 shows identification rate of both Setup-R and Setup-B, with different
segment sizes $L_{seg}=10,20,30,60,90$ s. In Setup-B, the identification rate
with $L_{seg}=10$ s was 67.7 %, which was almost the same as the result with
$L_{seg}=60$ s in Setup-R. The highest identification rate, 87.2 %, was
achieved with $L_{seg}=90$ s, where corresponding Kappa was $\kappa=0.86$
(Almost Perfect agreement) in Setup-B.

Figure 7: The identification rate with different segment size in Setup-R and Setup-B. The error bars indicate the standard error. TABLE X:  Recording details, accuracy and HTER in the verification problem, and sensitivity in the identification problem for each subject with different training and validation data in Setup-R |  | Impedance |  |  |  Verification (Client-Imposter, $M=2$) | Identification
---|---|---|---|---|---|---
$S_{R}$ | Interval | (k$\Omega$) | Time∗ |  $Y_{T}$:Day1 $\rightarrow$ $Y_{V}$:Day2 |  $Y_{T}$:Day2 $\rightarrow$ $Y_{V}$:Day1 | ($M=15$)
Sub | day | Day1 | Day2 | Day1 | Day2 | AC(%) | HTER(%) | AC(%) | HTER(%) | SE(%)
1 | 5 | $<9$ | $<10$ | M | A | 98.5 | 6.0 | 100 | 0.0 | 94.4
2 | 6 | $<6$ | $<6$ | A | A | 94.8 | 28.6 | 97.0 | 11.9 | 61.1
3 | 8 | $<5$ | $<8$ | A | A | 98.5 | 0.8 | 94.8 | 33.8 | 66.7
4 | 7 | $<9$ | $<10$ | A | M | 95.6 | 28.2 | 96.3 | 7.2 | 66.7
5 | 7 | $<8$ | $<4$ | A | A | 96.3 | 17.5 | 97.0 | 22.2 | 61.1
6 | 7 | $<12$ | $<14$ | A | A | 91.1 | 20.2 | 97.8 | 6.4 | 77.8
7 | 15 | $<12$ | $<13$ | M | M | 98.5 | 11.1 | 96.3 | 22.6 | 66.7
8 | 6 | $<11$ | $<11$ | M | M | 91.1 | 35.8 | 91.9 | 35.4 | 33.3
9 | 7 | $<10$ | $<13$ | A | A | 96.3 | 12.3 | 95.6 | 12.7 | 77.8
10 | 8 | $<9$ | $<9$ | M | M | 91.9 | 35.4 | 93.3 | 8.7 | 61.1
11 | 6 | $<11$ | $<13$ | A | A | 95.6 | 17.8 | 94.8 | 13.1 | 72.2
12 | 5 | $<13$ | $<11$ | M | M | 96.3 | 17.5 | 99.3 | 0.4 | 83.3
13 | 7 | $<13$ | $<9$ | M | M | 95.6 | 17.8 | 97.8 | 11.5 | 72.2
14 | 6 | $<8$ | $<9$ | M | A | 97.8 | 11.5 | 95.6 | 33.4 | 55.6
15 | 5 | $<9$ | $<13$ | A | A | 94.1 | 13.5 | 91.9 | 25.0 | 66.7
Ave. | 7 | - | - | - | - | 95.5 | 18.2 | 96.0 | 16.3 | IR = 67.8 %
_Setup-R: rigorous_ , $L_{seg}=60$ | Overall AC=95.7 %, HTER=17.2 % |  $\kappa$ = 0.65
∗M: Morning (9-12), A: Afternoon (12-18) |  bold: best result, italic: worst result, out of 15 subjects.

##  V Discussion

This study aims to establish a repeatable and highly collectable EEG
biometrics using a wearable in-ear sensor. We considered a biometric
verification problem, which was cast into a one-to-one client-imposter
classification setting. Notice that, as described in Section III-F, before
classification, the validation matrix was normalised column-wise to the range
$[0,1]$ using the corresponding maximum/minimum values of the training matrix.

###  V-A Verification with different segment sizes

Firstly, the classification results were compared for different segment
lengths $L_{seg}$, shown in Table VI. Within the same setup, i.e. Setup-R or
Setup-B, the performance of HTER and AC increased with the segment length,
although the results with $L_{seg}=60$ s and $L_{seg}=90$ s are almost the
same. Longer segments allowed for more data epochs to be averaged over, hence
the EEG noise inference for classification diminished and the inherent EEG
characteristic were able to be captured by averaging. However, a longer
segment length also implies a longer recording time, which is not ideal for
feasible EEG biometrics. Compared to the results in Setup-R and Setup-B for
the same segment length, both the HTERs and accuracy (ACs) of Setup-B were
clearly better than those of Setup-R. In terms of client discrimination, the
decrease in FRR was significant from Setup-R to Setup-B. In Setup-B, a larger
number of client segments was correctly classified (see TP) than in Setup-R.
In setup-R, only the result with $L_{seg}=60$ s achieved TP > (FN + FP), which
indicates that the likelihood of making a true positive verification is higher
than making a false verification; in Setup-B, the shortest segment size,
$L_{seg}=10$ s, achieved TP > (FN + FP).

The difference between Setup-R and Setup-B was that the training matrices
$Y_{T}$ in Setup-B included the trials which were recorded on the same day as
the validation trial. In other words, the assigned validation data (trial) and
the part of assigned training data (trials) were recorded within 5 - 10
minutes in the same environment. Therefore, the training matrix contains
significantly similar EEG recordings to the validation matrix in Setup-B, and
this leads to a higher classification performance than the classification in
Setup-B.

With an increase in the segment size $L_{seg}$, the number of segments per
recording trial, $N$, became smaller, especially for $N=2$ with the segment
size $L_{seg}=90$ s; therefore, the training matrix only contains six (2
$\times$ 3 trials) examples of client data in Setup-R (c.f. ten examples in
Setup-B), which might not be enough for training client data. Hence, the
performance with $L_{seg}=90$ s was slightly lower than that with $L_{seg}=60$
s in Setup-R.

###  V-B Verification with different classifiers

Table VIII shows the classification comparison among the minimum cosine
distance methods, LDA and SVM. The SVM was used as a parametric classifier;
firstly, the optimal hyper-parameters (see details in Table IV) were selected
from 5-fold cross-validation within the training matrix, and then weight
parameters based on these chosen hyper-parameters were obtained. Notice that
we could tune the classifier in different ways, e.g. in order to minimise
false acceptance or minimise false rejection. The optimal tuning in this study
was performed so as to maximise class sensitivities, i.e. maximise the number
of TP and TN elements, which resulted in minimum HTERs. In both Setup-R and
Setup-B, the FARs by SVM were smaller than those achieved by both the minimum
cosine distance and the LDA, because the tuning was performed for maximising
TN elements. Since the number of imposter elements was fourteen times bigger
than the number of clients in both Setup-R and Setup-B (i.e. chance level was
14/15), the SVM parameters were tuned for higher sensitivity to imposters. As
a result, the FRR by SVM, which were related to client sensitivity given in
Table IX, were higher than those achieved by both minimum cosine distance and
LDA in Setup-R.

In Setup-B, as mentioned above, the training matrix contains the data from the
same recording day, which are more similar EEG patterns than the data obtained
from a different recording day. Therefore, the SVM model chose hyper-
parameters and weight parameters from the training matrix, so as to better the
validation data in Setup-B, which led to higher performance than by both the
minimum cosine distance and LDA.

Notice that, as described before, tuning of the hyper-parameters was performed
within the training matrix, then the so-obtained hyper-parameters were used
for finding the optimal weight parameters within the training matrix. The same
hyper-parameters and weight parameters were used for classifying the
validation matrix. This setup is applicable for feasible EEG biometrics
scenarios in the real-world.

###  V-C Validation including non-registered imposters

In Table IX, the confusion matrices for the client matrix $Y_{VC}$ and
imposter matrix $Y_{VI}$ from dataset $S_{R}$ and imposter matrix
$Y_{VI_{N}}$, are given, which were then used for a comparison between Setup-R
and Setup-B. Compared to the results obtained within the same classifier
(minimum cosine distance, LDA, SVM) in Setup-R and Setup-B, the true positive
rate (TPR) of clients $Y_{VC}$ and the sensitivity of imposter $Y_{VI}$ from
dataset $S_{R}$ in Setup-B were higher than those in Setup-R. As described
before, in Setup-B, the two client trials from the same recording day as the
validation trial were included into the training matrix, and therefore more
segments were correctly classified. In contrast, the TPRs of imposter
$Y_{VI_{N}}$ from dataset $S_{N}$ by both the LDA and SVM in Setup-B were
almost the same to those in Setup-R; 89.9 % and 88.7 % for LDA, 96.2 % and
96.3 % for SVM. Compared to the TPR between two imposter data ($Y_{VI}$ and
$Y_{VI_{N}}$), regardless of the classifiers, the TPR of $Y_{VI}$ were higher
than those of $Y_{VI_{N}}$. Since the imposter data from $S_{N}$ were not
included in the training matrix, the more $S_{N}$ data were misclassified as
‘client’ than $S_{R}$ data misclassified as ‘client’.

However, in the real-world scenarios for biometrics, imposters are not always
‘registered’. The lower TPR for $Y_{VI_{N}}$ means that the application is
inadequate for attack from non-registered subjects. One potential way to
overcome the vulnerability of the minimum cosine distance classifier is by
introducing threshold for classification. If the nearest distance is larger
than the given distance parameter, the segment is excluded from the
classification or is classified as imposter.

###  V-D Client-Imposter verification results per subject

For subject-wise classification, Table X summarises classification results
obtained by the minimum cosine distance in Setup-R, for different training-
validation scenarios. The results varied across subjects and for training-
validation configurations between 91.1 % to 100 % of AC and between 0.0 % to
35.8 % of HTER.

The size of viscoelastic earpiece was the same for twenty subjects (both
$S_{R}$ and $S_{N}$ subjects), therefore all the subjects were able to wear it
comfortably. The upper bounds of the electrode impedance over three recordings
per day of each participant are given in Table X (Impedance part). The highest
performance was achieved by Subject 1, with maximum impedances of 9 k$\Omega$
and 10 k$\Omega$ for the 1st and 2nd recording, respectively. Even though the
impedances for Subject 2 and Subject 5 were smaller than those for Subject 1,
the corresponding performance was below average over fifteen subjects.
Besides, the lowest performance was exhibited by Subject 8, for whom the
impedances were smaller than 11 k$\Omega$ for Day1 and 11 k$\Omega$ for Day2.
Figure 8 shows average PSDs for Subject 8 – observe that the PSDs for the EEG
recorded on Day2 (blue) is slightly larger than those on Day1 (red).

Figure 8:  Power spectral density for the in-ear EEG Ch1 (left) and the in-ear
EEG Ch2 (right) of Subject 8. The thick lines correspond to the averaged
periodogram obtained by the all recordings from the 1st day (red) and the 2nd
day (blue), whereas the thin lines are the averaged periodogram obtained by a
single trial.

###  V-E Biometrics identification

In terms of biometrics identification results, a one-to-many subject-to-
subject classification problem, the average sensitivity over fifteen subjects,
i.e. the identification rate, was 67.8 % in Setup-R with $L_{seg}=60$ s, as
shown in Table X (right column). Figure 7 illustrates the identification rates
of both Setup-R and Setup-B, with different segment sizes
$L_{seg}=10,20,30,60,90$ s. The identification rate increased with segment
length, although the results with $L_{seg}=60$ s and $L_{seg}=90$ s are almost
the same.

Notice that the performances with $L_{seg}=10$ s in Setup-B and $L_{seg}=60$ s
in Setup-R were almost the same, 67.7 % and 67.8 %, respectively. The highest
identification rate in Setup-B was 87.2 % with $L_{seg}=90$ s. Indeed, the
training matrix for Setup-B included the trials which were recorded at the
same day as the validation trial; therefore the performance was better than in
Setup-R.

In a previous biometrics identification study, Maiorana et al. [22] analysed
19 channels of EEG during EC tasks in three different recording days, and
achieved the rank-1 identification rate (R1IR) of 90.8 % for a segment length
45 s. Notice that it is hard to compare the performance with our approach,
because the number of channels was very different, as 19 scalp EEG channels
covered the entire head vs our 2 in-ear EEG channels embedded on an earplug.
Therefore, although our results were lower, the proof-of-concept in-ear
biometrics emphasised the _collectability_ aspect in fully wearable scenarios.

###  V-F Alpha attenuation in the real-world scenarios

One limitation of using the alpha band, is the sensitivity to drowsiness, a
state where the alpha band power is naturally elevated. For illustration,
Figure 9 shows the PSD obtained from a subject, calculated by Welch’s
averaging periodogram method. The subject slept during one recording, then the
subject was woken up and another recording started less than 10 minutes after
the first recording. The PSD graphs in Figure 9 are overlapped except for the
alpha band; the alpha power observed during the ‘sleepy’ recording trial was
smaller than that at the ‘normal’ recording, thus demonstrating the alpha
attenuation due to fatigue, sleepiness, and drowsiness. The alpha attenuation
is well known in the research in sleep medicine [36, 26], where it is
particularly used to monitor sleep onset.

Figure 9:  Power spectral density for the in-ear EEG Ch1 (left), and the in-
ear EEG Ch2 (right) of one subject. The thick lines correspond to the averaged
periodogram obtained by the recordings from ‘sleepy’ trial (red) and ‘normal’
trial (blue). Observe that the alpha power attenuated during the ‘sleepy’
trial.

##  VI Conclusion

We have introduced a proof-of-concept for a feasible, collectable and
reproducible EEG biometrics in the community by virtue of an unobtrusive,
discreet, and convenient to use in-ear EEG device. We have employed robust PSD
and AR features to identify an individual, and unlike most of the existing
studies, we have performed classification rigorously, without mixing the
training and validation data from the same recording days. We have achieved
HTER of 17.2 % with AC of 95.7 % with segment sizes of 60 s, over the dataset
from fifteen subjects.

The aspects that need to be further addressed in order to fulfil the
requirements for ‘truly wearable biometrics’ in the ‘real-world’ will focus on
extensions and generalisations of this proof-of-concept to cater for:

  * •

Intra-subject variability with respect to the circadian cycle and the mental
state, such as fatigue, sleepiness, and drowsiness;

  * •

Additional feasible recording paradigms, for example, evoked response
scenarios;

  * •

Truly wearable scenarios with mobile and affordable amplifiers;

  * •

Inter- and intra-subject variability over the period of months and years;

  * •

Fine tuning of the variables involved in order to identify the optimal
features and parameters (segment length, additional EEG bands).

##  VII Acknowledgement

We wish to thank the anonymous reviewers for their insightful comments.

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