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fNIRS: A New Modality for Brain Activity-Based Biometric Authentication
Abdul Serwadda Vir V. Phoha Sujit Poudel Leanne M. Hirshfield
Danushka Bandara Sarah E. Bratt Mark R. Costa
Syracuse University, Syracuse, NY 13210
{aserwadd,vvphoha,spoudel,lmhirshf,dsbandar,sebratt,mrcosta}@syr.edu
Abstract
There is a rapidly increasing amount of research on
the use of brain activity patterns as a basis for biomet-
ric user verification. The vast majority of this research is
based on Electroencephalogram (EEG), a technology which
measures the electrical activity along the scalp. In this
paper, we evaluate Functional Near-Infrared Spectroscopy
(fNIRS) as an alternative approach to brain activity-based
user authentication. fNIRS is centered around the measure-
ment of light absorbed by blood and, compared to EEG, has
a higher signal-to-noise ratio, is more suited for use during
normal working conditions, and has a much higher spatial
resolution which enables targeted measurements of specific
brain regions. Based on a dataset of 50 users that was anal-
ysed using an SVM and a Na¨
ıve Bayes classifier, we show
fNIRS to respectively give EERs of 0.036 and 0.046 when
using our best channel configuration. Further, we present
some results on the areas of the brain which demonstrated
highest discriminative power. Our findings indicate that
fNIRS has significant promise as a biometric authentication
modality.
1. Introduction
Given the well known drawbacks of password-based au-
thentication, there is now a significant amount of interest
in the Active Authentication paradigm (e.g., see recent re-
search efforts such as DARPA’s Active Authentication pro-
gram [1], and AFRL’s Mobile Android Multi-Biometric Ac-
quisition program [2]). The major security benefit offered
by Active Authentication (AA) stems from the fact that a
user is monitored throughout a session of interaction with
the computing device, making it exceedingly difficult for a
masquerader to pose as the genuine user. This is in stark
contrast to a password-based authentication setting where a
user is verified once before being granted access, leaving the
system vulnerable to any adversary holding the password.
While AA is perhaps best understood from the perspec-
tive of behavioral patterns (e.g., keystroke, mouse and touch
dynamics), recent work has revealed that neural patterns,
such as those manifested by a user’s brain activity during
different mental tasks, also hold significant promise as an
AA modality. Monitoring these patterns using Electroen-
cephalogram (EEG) sensors, several studies have show-
cased classification accuracies in the 80% to 90% range
(e.g., see [16][14]) depending on the tasks being performed
by the users during authentication.
In this paper, we extend the state-of-the-art in brain
activity-based user authentication and introduce Functional
Near-Infrared Spectroscopy (fNIRS) as an AA modality.
The basic mechanism behind fNIRS is that neural activity
in the brain during different mental tasks causes changes
in blood flow, which can be measured by monitoring the
changes in (near-infrared) light absorbed by the blood (see
details in Section 3). Relative to EEG, fNIRS has a plethora
of advantages, some of which include a higher level of prac-
ticality for use in normal working conditions, a much higher
signal-to-noise ratio, and significantly higher spatial resolu-
tion (see details in Section 3). Although fNIRS is fast be-
coming mainstream in domains such as Human Computer
Interaction (see some recent works [11][23]), it is surpris-
ingly yet to receive significant attention from the biometrics
authentication community.
The only work to have examined fNIRS as a user veri-
fication modality is the 2-page abstract by Heger et al. [9].
Different from our work however, the abstract reported re-
sults based on a very small user population (of just 5 users),
used a very small fNIRS device (with less than a sixth of the
number of channels used in this work), did not provide any
insights into the traits depicted by the different features or
brain regions, and focused on the user identification prob-
lem. To the best of our knowledge, ours is the first paper to
examine fNIRS as a user authentication modality, let alone
to explore the credentials of fNIRS as a biometric modality
in details on a large dataset. The contributions of this paper
are summarized below:
1. Based on a 50-user dataset collected using a 52-
channel fNIRS device, we evaluate fNIRS as an au-
thentication modality. Respectively using an SVM and
Na¨
ıve Bayes classifier, we show fNIRS to give Equal
Error Rates of 0.043 and 0.063 when data from all 52
channels is used for authentication and Equal Error
Rates of 0.036 and 0.046 when a sub-set of channels
having highest discriminative power is used. Our work
represents the first steps towards the use of fNIRS as
an AA user authentication modality.
2. We analyze the variations of feature discriminative
power across brain regions and present findings on
which regions of the brain discriminated between users
best while they completed the simple addition tasks
that were used during our experiments. The most pre-
dictive brain regions for authentication were regions
that have been found to be highly sensitive to addition
tasks in prior neuroscience literature.
The rest of the paper is organized as follows. In Section
2, we discuss related work and provide insights into non-
invasive brain measurement in Section 3. We describe our
data collection experiments in Section 4 and present the
fNIRS performance evaluation results in Section 5. We fi-
nally make our conclusions in Section 6.
2. Related Work
A great deal of prior research has explored biometric au-
thentication using a range of physiological and behavioral
user metrics. Approaches to biometric authentication and
identification vary with respect to the features and tech-
niques used. In terms of features, users periocular regions
[4], eye gaze patterns [20], cardiac biometric patterns [8],
fingerprints [5], and facial features [12] or combinations
thereof, have all been used to support the development of
automated authentication and identification systems.
Prior research has also explored brain activity measure-
ment as a way to authenticate users. Ideally, a brain mea-
surement device suitable for biometric authentication and
identification under normal working conditions would be
non-invasive and portable. It would have fast temporal res-
olution and high spatial resolution, enabling the localiza-
tion of brain activation in specific functional brain regions.
The EEG has been the most studied device used to measure
brain activity during naturalistic human-computer interac-
tions (e.g., see [16][14]), while the relatively new fNIRS
device has been gaining momentum in several research do-
mains in recent years. The EEG measures the waves gener-
ated by cascading electrochemical signals produced by the
firing of neurons while the fNIRS captures blood flow to
brain regions to support the firing of neurons.
EEGs have been available for over one hundred years in
the research domain, while fNIRS was only introduced in
the past twenty-five years; therefore EEGs are much more
likely to be used in biometric verification research. Prior re-
search using EEGs for biometric verification involves brain
measurement while the subjects are at rest [16][3] or en-
gaged in a task that stimulates brain regions associated with
verbal [16] spatial [18], or arithmetic work [18]. However,
when compared to fNIRS, EEG devices are more suscepti-
ble to noise, both from ambient sources (e.g., electrical sys-
tems in buildings), and motion artifacts. Also, EEGs have
lower spatial resolution than fNIRS, making it difficult to
determine the actual regions of the brain that are stimulated
at any given time. EEG’s limitations make fNIRS an attrac-
tive alternative for biometric verification using brain mea-
surements.
As mentioned previously, there has only been one prior
publication studying the potential of fNIRS for user verifi-
cation. This 2-page abstract by Heger et al. [9] used a very
small user population (n=5) for user identification, making
it difficult to determine the scalability of the results and pro-
viding no insights at all into the user authentication prob-
lem that is the focus of this paper. Additionally, the fNIRS
device used by Heger et al. was only capable of measur-
ing 8 points in users brains (our configuration, described
below, collects 52 measurement locations), and they do not
include any detail about the actual regions of the brains that
were predictive of user authentication on their dataset.
3. Non-Invasive Brain Measurement
There are several brain measurement devices available
in medical and research domains. These devices monitor
brain activation by measuring several biological metrics.
When a stimulus is presented, neurons fire in the activated
region(s) of the brain, causing an electric potential, an in-
crease in cerebral blood flow in that region, an increase in
the metabolic rate of oxygen, and an increase in the volume
of blood flow. All of these factors contribute to the blood
oxygen level dependent (BOLD) signal, which can be de-
tected (in various forms) by a number of brain measurement
techniques such as fMRI, fNIRS, and PET[6]. Ideally, a
brain measurement device suitable for measuring brain ac-
tivity in typical HCI activities would be non-invasive and
portable. It would have extremely fast temporal resolution
(for use in adaptive systems) and it would have high spa-
tial resolution, enabling the localization of brain activation
in specific functional brain regions. Electroencephalograph
(EEG) and fNIRS are the two most popular devices for non-
invasive imaging of the brain. However, when compared
to EEG, fNIRS has higher spatial resolution, lower set-up
time, and a higher signal-to-noise ratio [19]. We focus on
fNIRS, as this is one of the best suited technologies for
non-invasive brain measurement during naturalistic human-
computer interactions.
3.1. Functional Near-Infrared Spectroscopy
As described above, fNIRS is a relatively new non-
invasive technique introduced in the late 1980s [7] to over-
2
come many of the drawbacks of other brain monitoring
techniques. The tool, still primarily a research modality,
uses light sources in the near infrared wavelength range
(650-850 nm) and optical detectors to probe brain activity.
Light source and detection points are defined by means
of optical fibers held on the scalp with an optical probe.
Deoxygenated (Hb) and oxygenated hemoglobin (HbO) are
the main absorbers of near infrared light in tissues during
hemodynamic and metabolic changes associated with neu-
ral activity in the brain. These changes can be detected
by measuring the diffusively reflected light that has probed
the brain cortex. fNIRS has been used in recent years to
measure a myriad of mental states such as workload, de-
ception, trust, suspicion, frustration, types of multi-tasking,
and stress [22][10].
4. Experiment
The goal of our experiment was to authenticate a partic-
ipant based solely on his or her previously acquired brain
data. Three mental tasks were chosen for the experiment,
as we were interested in learning which tasks yielded brain
data that was more predictive of participant identification.
These tasks, and the experiment protocol, are described
next.
4.1. Experiment Tasks
In the experiment, three conditions, or tasks, were cho-
sen based upon a review of psychological and neuroscience
literature to produce consistent patterns of brain activation
for the later identification of subjects. All experiment tasks
were created using Microsoft Powerpoint. The first condi-
tion that subjects were given was called Phone-number re-
call. During this task, participants were instructed to think
of their phone number repeatedly for a twenty second pe-
riod of time. The next condition was called Addition. This
task began with a slide instructing participants to start with
x, where x was a small number under 10, such as 5. Next,
new slides appeared with instructions such as add 6 or add
9 (with no values greater than 9 to be added at a time). Each
addition slide was displayed for 2 seconds, and participants
were told to keep a running sum as new numbers appeared.
The last slide of the addition section instructed the partici-
pant to tell the experimenter the total sum of all of the num-
bers. The third mental task was called Controlled Rest. Par-
ticipants were told to relax and clear their minds during this
task. The controlled rest task was included in order to deter-
mine if it was possible to identify participants during their
resting state.
4.2. Experiment Protocol
Fifty subjects (37 male) participated in the experiment.
Subjects were students from a school in the Northeast. In-
formed consent was obtained, and participants were com-
pensated for their time. We used a randomized block de-
sign, with the three experimental conditions described pre-
viously. Each task lasted 20 seconds and a ten-second
rest period was placed after each task, allowing partici-
pants brains to return to baseline. In each measurement ses-
sion, there were four experimental blocks (3 conditions x 4
blocks = 12 tasks per session).
Each subject completed a total of four measurement ses-
sions (see Figure 1). The first and second sessions were
completed in the morning and afternoon, respectively, of
data collection day one. The third and fourth sessions were
completed in the morning and afternoon, respectively on
data collection day two, which was completed two weeks
after data collection day one.
Place probe on participant
Controlled Rest (20s)
Addition (20s)
Recite Phone Number(20s)
Rest (10s)
X 4 blocks
Remove Probe
Rest (10s)
Rest (10s)
Figure 1: Model of the chronology of tasks users undertook
during each measurement session.
All sessions were identical in their experiment layout and
the fNIRS cap was newly placed on the subject at the be-
ginning of each session, with the probe centered on each
participant’s forehead. Before beginning the first session
of the experiment, subjects were informed about the tasks
and given an opportunity to practice the tasks. They were
told that the tasks would appear in a random order. They
were then given the opportunity to ask any questions they
had about the experiment. Once it was clear that the partic-
ipants understood the tasks, the fNIRS cap was placed on
the participant and the PowerPoint presentation was started.
The fNIRS device used in this experiment was Hitachi Med-
ical’s ETG4000, with a sampling rate of 2Hz. Participants
wore a 52-channel cap (see Figure 2), comprised of 17 light
sources and 16 detectors.
3
Figure 2: A subject wearing the 52-channel fNIRS device in our lab
5. Performance Evaluation
5.1. Feature Extraction and Analysis
For each of the 52 channels, the raw light intensity
dataset was preprocessed to generate changes in oxyhe-
moglobin, deoxyhemoglobin, and total hemoglobin. All
subsequent analysis in this paper is based on the changes in
oxyhemoglobin (∆HbO) which were obtained while users
undertook the addition task (see Section 4.1 for details of
the tasks). We first carried out a min-max normalization to
scale all channels to the range 0−1before feature extrac-
tion. From each channel we then extracted eight features
from the 41 data points registered during each 20-second
instance of the mathematical task. These features were: (1)
standard deviation of first 10 points, (2) standard deviation
of the last 10 points, (3) standard deviation of the points
in the middle segment, (4) mean of the first 10 points, (5)
mean of the last 10 points, (6) mean of the points in the mid-
dle segment, (7) maximum value and (8) minimum value.
To determine which features had the highest discrimi-
native power, we used the relative mutual information, IR
between each feature and the class labels (also used in [17]).
Let Fdenote a vector containing the outputs of a given fea-
ture across the population and Cdenote a vector of class
labels. IRis computed as the ratio of I(F;C)to H(C)
where I(F;C)is the mutual information between Fand C
and H(C)is the entropy of C.IRvaries between 0 and 1,
with values tending towards 1 indicating highest discrimi-
native power. Fbeing a continuous variable, we discretized
it (using 20 equally spaced bins) before computing I(F;C).
Figure 3 shows how the discriminative power of two fea-
tures (i.e., the mean of the first ten points and the standard
deviation of the last ten points) varied across the 52 chan-
nels. Figure 3(a) shows the locations of each of the 52 chan-
nels relative to each other and relative to the position of the
eyes, while Figures 3(b) and 3(c) respectively show how IR
varied across the channel space for the two above mentioned
features. A dark red color indicates a region that had very
high discriminative power while a dark blue color indicates
a region of very low discriminative power. Note that the
IRvalues are represented as percentages (see color to IR
mapping at the extreme right of the plots). From the figure,
it is apparent that the mean of the first ten points separated
users better than the standard deviation of the last ten points
(which was one of our worst performing features). Regard-
less of the performance gap between the two features, the
figures reveal an interesting trait: regions around the lower
part of the face were more discriminative than those at the
upper parts. In Section 5.2 we leverage this information to
fine-tune our classification methodology. Note that the fea-
ture analysis described above was only applied to a training
subset of the dataset. The question of whether these feature
analysis findings generalize to the testing dataset is one of
those questions to be addressed in the next section.
5.2. Authentication Results
5.2.1 Mean and User-level Error Rates
To cut down on the high dimensional feature space and also
speed up the learning process, we dropped the two worst
performing features (on basis of mean IR) and retained
5 features per channel. Classifier training (i.e., template
building) was done based on data collected on the first day
while testing was done based on data collected on the sec-
ond day (recall experiment sessions in Section 4.2). To con-
duct impostor tests against a given user, we used 15 samples
randomly drawn from the other (49) users. To test a user’s
template against the user’s own samples (i.e., genuine test-
ing), we used all data provided by the user on the second
day.
Table 1 shows the mean Equal Error Rates (EER) ob-
tained with the SVM and Na¨
ıve Bayes classifiers when clas-
4
Red: 32, 44, 34, 45, 46, 47, 48, 49, 50, 21, 52, 36, 37, 38, 39, 40, 26, 27, 12, 15, 16, 21
White: 4, 5, 6, 9, 10, 19, 28, 29, 42, 43
Blue: 33, 35, 41, 22, 23, 24, 25, 30 31, 11, 13, 14, 17, 18, 20, 1, 2, 3, 7, 8
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Right Eye
Left Eye
Channels
Detectors
Sources
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(a) Locations of the 52 channels relative to each other and relative to the two eyes (approximately).
(b) Color map showing how the discriminative power of the mean of the first ten points varied across the 52 channels (or brain
regions). The red regions cover a large area relative to the blue regions, meaning that there is a good number of channels for which
this feature was highly discriminative.
(c) Color map showing how the discriminative power of the standard deviation of the last ten points varied across the 52 channels
(or brain regions). The blue regions cover a larger area relative to their coverage area in Figure 3(b), meaning that this feature was
not as discriminative as the feature represented in Figure 3(b).
Figure 3: Illustrating the discriminative power of two features (the mean of the first ten points and the standard deviation of
the last ten points) across the 52 channels. X is a measure of the horizontal distance from the top left corner of Figure 3(a)
while Y is a measure of the vertical distance from the same point. Locations such as the white space between channels 1,
11, 12 and 22 (see Figure 3(a)), are represented by the mean value of IRof the four neighboring channels for continuity and
clarity of the color map.
Classifier Mean EER % Change
All Channels Best Channels in EER
SVM 0.043 0.036 17.1
Na¨
ıve Bayes 0.063 0.046 28.3
Table 1: Comparing the mean EER of the two classification algorithms when all channels were used for classification with
the mean EER obtained when only a sub-set of channels (i.e., the most discriminative channels) were used.
sification was done based on two scenarios: when all chan-
nels were used for user classification and when only the
channels which exhibited the highest discriminative power
in the previous classification step were used. The EER is the
error rate at the threshold when the False Reject Rate (FRR)
equals the False Accept Rate (FAR) and is very widely used
to evaluate the performance of biometric authentication sys-
tems (e.g., see [15][17]). The EER ranges between 0 and 1
(or 0 and 100 on a percentage scale), with values close to
zero pointing to a system that performs well at separating
users.
When all channels were used, both classifiers had EERs
of less than 7% which reduced to under 5% when the best
channels were used. The reductions in EER seen when a
sub-set of channels (mostly the lower channels; see Figure
3) were used confirms the benefits of our feature analysis
5
step (i.e., a performance boost and a lower computation
overhead) and suggests that blood flow around the region
just above eye-level might be the best (relative to blood flow
at other regions of the head) at discriminating between users
undertaking simple mathematical tasks such as addition. A
more rigorous evaluation of these regions of interest and
how they relate to different tasks performed by the authen-
ticated user is part of our ongoing research. Overall, these
low error rates depict the promise of fNIRS as a continuous
authentication modality which could serve as extra layer of
security to the traditional security mechanisms e.g., pass-
words.
0 0.05 0.1 0.15 0.2 0.25 0.3
0
20
40
60
80
100
EER
CDF
Naive Bayes
SVM
Figure 4: CDF of the EERs obtained across the population
for the two classifiers.
While the above described results provide insights into
the mean error rates over the full population, it is interesting
to also explore how each of the 50 users in our experiment
performed. Figure 4 shows a CDF of the user-level EERs
across the population for both classifiers. For both classi-
fiers, over 60% of the population had EERs less than 0.05,
while under 20% of the population had EERs greater than
0.1. The large proportion of users with very low EERs indi-
cates that: (1) a significant number of users had consistent
brain activity patterns over the four measurement sessions
of our study, and that (2) the mean EER seen across the pop-
ulation could perhaps have been tremendously improved if
the small group of users who for some reason (e.g., not con-
centrating on the tasks) had inconsistent brain activity pat-
terns over the four sessions had been excluded prior to our
authentication evaluations.
5.2.2 Impact of Failure-to-Enroll Policy
The second conclusion made in the previous section
prompts the following question: How would the mean error
rate across the population change if users who exceeded a
certain threshold EER were systematically barred from en-
rolling onto the system? While we do not have explicit in-
formation on which users did not concentrate on the tasks as
instructed, it is reasonable to assume that a carefully tuned
failure-to-enroll policy would have a good chance of elim-
inating these kinds of users and any users who might have
concentrated on the tasks but perhaps just did not have the
0 0.05 0.1 0.15 0.2
0
10
20
30
40
50
# of Enrolled Users
0 0.05 0.1 0.15 0.2
0
0.01
0.02
0.03
0.04
0.05
Mean EER
Cut−off EER
# of Enrolled users
Mean EER
(a) Impact of Failure-to-enroll policy on the performance of the
Na¨
ıve bayes classifier.
0 0.05 0.1 0.15 0.2 0.25
20
40
60
# of Enrolled Users
0 0.05 0.1 0.15 0.2 0.25
0
0.02
0.04
Mean EER
Cut−off EER
# of Enrolled users
Mean EER
(b) Impact of Failure-to-enroll policy on the performance of the
SVM classifier.
Figure 5: Illustrating how a failure-to-enroll policy at dif-
ferent thresholds affects the classifier Error Rates.
required consistency of brain activity patterns. In a real
fNIRS-based authentication system, failure-to-enroll deci-
sions would be made based on observations (e.g., EERs)
made on preliminary data collected before the enrollment
phase.
Figure 5 shows how a failure-to-enroll policy impacted
the EERs of the two verifiers at different cut-off thresholds.
Figure 5(a) shows that when all users who had an EER ex-
ceeding 0.1 were excluded from the system, the mean EER
dropped from around 0.045 to just over 0.025 (an improve-
ment of over 40%) yet about 40 users (i.e., 80% of the orig-
inal population) were still able to enroll onto the system.
When the cut-off EER is reduced to 0.05, the mean EER of
the system reduces further to 0.01 (a change of 67% rela-
tive to the original EER) with 60% of the population able to
enroll. A slightly less dramatic trend is seen with the Na¨
ıve
Bayes classifier (Figure 5(b)), however the fact that barring
a small number of users from enrolling onto the system sig-
nificantly improves the performance of the fNIRS authenti-
cation system is still apparent.
6. Conclusions
In this paper, we evaluated fNIRS as a biometric au-
thentication modality based on data collected from 50 users
while they carried out simple arithmetic tasks. When we
used data from all 52 channels of the Hitachi Medical’s
ETG4000 fNIRS device, we obtained mean EERs of 0.043
and 0.063 respectively for the SVM and Na¨
ıve Bayes clas-
6
sification algorithms. When we used data from a sub-set of
channels having the highest individual discriminative power
(as measured from the Relative mutual information metric)
the mean EERs of the two classifiers respectively dropped
to 0.036 and 0.043. While there is still a need to evaluate
fNIRS for a wider range of mental tasks, these results sug-
gest that fNIRS holds promise as an AA modality. A major
part of our ongoing research is to carry out analysis on a
wider variety of tasks and to more rigorously evaluate the
dependence of authentication performance on specific brain
regions.
7. Acknowledgment
A. Serwadda, V. V. Phoha and S. Poudel were in part
supported by DARPA Active Authentication grant FA8750-
13-2-0274.
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