Using Brain Waves as New Biometric Feature for Authenticating a Computer User in Real-Time

Kusuma Mohanchandra, Lingaraju G M, Prashanth Kambli & Vinay Krishnamurthy
International Journal of Biometrics and Bioinformatics (IJBB), Volume (7) : Issue (1) : 2013 49
Using Brain Waves as New Biometric Feature for Authenticating
a Computer User in Real-Time
Kusuma Mohanchandra kusumalak@gmail.com
Associate Professor/Department of Computer Science & Engineering
Dayananda Sagar of Engineering
Bangalore, 560078, India
Lingaraju G M gmlraju@gmail.com
Professor/Department of Information Science & Engineering
M S Ramaiah Institute of Technology
Prashanth Kambli prash.kamblil@gmail.com
Assistant Professor/Department of Information Science & Engineering
M S Ramaiah Institute of Technology
Vinay Krishnamurthy vinayk.url@gmail.com
Student, Department of Computer Science
Stony Brook University
Stony Brook - 11790, NY, USA
Abstract
In this paper we propose an Electroencephalogram based Brain Computer Interface as a new
modality for Person Authentication and develop a screen lock application that will lock and unlock
the computer screen at the users will. The brain waves of the person, recorded in real time are
used as password to unlock the screen. Data fusion from 14 sensors of the Emotiv headset is
done to enhance the signal features. The power spectral density of the intermingle signals is
computed. The channel spectral power in the frequency band of alpha, beta and gamma is used
in the classification task. A two stage checking is done to authenticate the user. A proximity value
of 0.78 and above is considered a good match. The percentage of accuracy in classification is
found to be good. The essence of this work is that the authentication is done in real time based
on the meditation task and no external stimulus is used.
Keywords: Cognitive Biometrics, Authentication, Brain Computer Interface,
Electroencephalogram, Power Spectral Density.
1. INTRODUCTION
In this computer driven era, with the increase in security threats, securing and managing the
resources has become a more complex challenge. Maintaining and managing access while
protecting the user’s identity and computer resources has become increasingly difficult.
Therefore, it is crucial to design a high security system that has a strong authentication process to
authenticate an individual. Authentication, verifying the user, who he claims to be, is the central to
all security systems. With the world getting ready to transit from Graphic User Interface (GUI) to
Natural User Interface (NUI) technology, where it is possible to communicate with computers by
using touch, gestures, voice, expressions, emotions and thoughts. In this context, we have made
an attempt to build an authentication system based on thoughts.

The common authentication approaches are those based on personal identification number (PIN)
and password. However, these can be easily compromised by methods such as ‘shoulder surfing’
[1]. The biometric approaches based on the biological characteristics of humans have distinct
advantages over traditional methods, as they cannot be hacked, stolen or transferred from one
person to another as they are unique for each person. But, as the biological characteristic of a
person change with time and age, it is required to find an alternative biometric trait that can
distinguish between individuals. Multimodal fusion for identity verification [2] has shown great
improvement compared to unimodal algorithms where they propose to integrate confidence
measures during the fusion process. These methods are used either to enhance the performance
of a multimodal fusion algorithm or to obtain a confidence level on the decisions taken by the
system.
Existing technologies mostly use fingerprints, speech, facial features, iris and signatures as a
base for an authentication or an identification system. These traits however, are known to be
vulnerable to falsification as it is possible to forge or steal. Therefore, new types of physiological
features that are unique and cannot be replicated are proposed [3] for an identification system.
This paper focuses its attention to the electroencephalogram (EEG) signal as a biometric. The
EEG based biometrics is widely being considered in security sensitive areas like banks, labs and
identification of criminal in forensic. It can be used as a component of National e-identity card in
government sector, as they have proven to be unique between people.
Brain-computer interface (BCI) is an emerging technology which aims to convey people's
intentions to the outside world directly from their thoughts, enhancing cognitive capabilities and is
a direct communication pathway between a brain and an external device. A common method for
designing BCI is to use EEG signals extracted during mental tasks [4]. EEG is the
neurophysiological measurement of electrical activity in the brain recorded by scalp electrodes
(sensors) and represents a summation of post-synaptic potentials from a large number of
neurons. Studies have shown that Brain wave pattern for each individual is unique and thus can
be used for biometric purpose. EEG-based biometry [5] is an emerging research topic. Very little
work has been done in this area, focusing more on person identification than person
authentication. Person authentication aims to accept or reject a person claiming an identity, i.e.
comparing a biometric data to one template, while the goal of person identification is to match the
biometric data against all the records in a database [6]. In our work, we have made an attempt to
authenticate a system, rather than identification. EEG is used to extract reliable features of brain
signals [7]. Brain waves measured by EEG represent a summary of brain electrical activity at a
recording point on the scalp i.e. the fusion of delta, theta, alpha/mu, beta and gamma waves in
frequency band.
FIGURE 1: User Wearing the Emotiv Epoc Headset.
In this work, we investigate the use of brain activity for person authentication. It has been studied
that the brain-wave pattern of every individual is unique [8] and the signals captured through the
EEG can be used for biometric authentication. Person authentication aims to accept or reject a

person claiming an identity. An EEG EPOC headset with 14 channels manufactured by Emotiv
Inc. is used for signal acquisition. The data acquired from these multi sensors are coordinated
and managed to give the desired performance. N. Xiong et al [9], presents a comprehensive
review of multi-sensor management in relation to multi-sensor information fusion, describing its
place and role in the larger context, generalizing main problems from existing application needs,
and highlighting problem solving methodologies. The purpose of data fusion [10] is to produce an
improved model or estimate of a system from a set of independent data sources.
We perform data acquisition, feature extraction, matching the feature vector with the stored
template all in real time. As data from multiple channels is fusion, we have used Power Spectral
Density as a reliable feature [11]. Hence Power Spectral Density is used as the key feature in this
work. After obtaining the features, Principal Component Analysis (PCA) is performed to obtain
relevant features from the high dimensional data. The obtained feature vector is then compared
against a previously stored feature vector for the same person using template matching. A two
stage checking is done to authenticate the user. A single biometric with multiple matches [12] is
considered. The match is considered good if the result of the comparison is greater than the
threshold value which has been set to 0.78 after repeated trials keeping in mind the need to
satisfy low False Acceptance Error (FAE) and False Rejection Error (FRE). The decision
threshold of a system is set so that the proportion of false rejections will be approximately equal
to the proportion of false acceptances called as Equal Error Rate [6]. We have developed a GUI,
to let a user lock his computer screen when required and unlock the same by recording his brain
activity (EEG signals) as a password for the system. This authentication system was successfully
demonstrated as a pilot project and proof of concept [13].
An identity authentication system has to deal with two kinds of events: either the person claiming
a given identity is the one who he claims to be (in which case, he is called a client), or he is not
(in which case, he is called an impostor). Moreover, the system may generally take two decisions:
either accept the client or reject him and decide he is an impostor [6]. The main aim is to keep the
False Acceptance Error (FAE) and the False Rejection Error (FRE) close to zero. The Education
Edition SDK by Emotiv Systems includes a research headset: a (plus CMS/DRL references,
P3/P4 locations) high resolution, neuro-signal acquisition by wireless sensors and processing
wireless neuroheadset. Channel names based on the International 10-20 locations are: AF3, F7,
F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8 and AF4. The Education Edition SDK [14] also
includes a proprietary software toolkit that exposes the APIs and detection libraries. The SDK
provides an effective development environment that integrates well with new and existing
frameworks.
FIGURE 2: Illustration of Location of Electrodes on the Emotiv Headset [14].

2. RELATED WORK
EEG based person authentication was first proposed by Marcel [6]. They proposed the use of
Power Spectral Density as the feature, and a statistical framework based on Gaussian Mixture
Models (GMM) and Maximum A Posteriori Model (MAP) Adaptation on speaker and face
authentication. The potential of their method is shown by simulations using strict train/test
protocols and results. Person identification based on spectral information [15] extracted from the
EEG is addressed by M. Poulos, et al, . Neural network classification was performed on real EEG
data of healthy individuals to experimentally investigate the connection between a person's EEG
and genetically specific information. The proposed method has yielded correct classification
scores in the range of 80% to 100%, showing evidence that the EEG carries genetic information
for person identification.
A novel two-stage biometric authentication [1] method was proposed by Ramaswamy
Palaniappan. The feature extraction methodology includes both linear and nonlinear measures to
give improved accuracy. Their results show that the combination of two-stage authentication with
EEG features has good potential as a biometric as it is highly resistant to fraud. Principal
Component Analysis (PCA) is used for dimension reduction of the feature vector keeping only the
most discriminatory features, as the features have a high degree of redundancy.
3. METHODOLOGY
FIGURE 3: Framework of the Model.
A conceptual framework of the present work is shown in figure 3.
3.1 Data Acquisition
EEG signals are recorded with the Emotiv EPOC headset which uses 14 integrated sensors
located at standard positions of the International 10-20 system (Fig: 2). Sensors are placed on
the scalp using a conductive gel, after preparing the scalp area by light abrasion to reduce
electrode-scalp impedance. The sampling rate is 128Hz [16]. The total time of each recording is
10 seconds. The subject is instructed to avoid blinking or moving his body during the data
collection to prevent the noise caused due to artifacts [17]. So, no artifact rejection or correction is
employed. Artifacts due to eye blinks produces a high amplitude signal called Electrooculogram
(EOG) that can be many times greater than the EEG signals required by us [18]. The dataset
from normal subjects are recorded for two active cognitive tasks during each recording session.
• Meditation activity: The subject is asked to meditate for a fixed period of time while his
brain waves are recorded.

• Math activity: The subject is given non-trivial multiplication problems, such as 79 times 56
and is asked to solve them without vocalizing or making any other physical movements.
The problems were designed so that they could not be solved in the time allowed [19].
3.2 Preprocessing and Feature Extraction
The EEG data is segmented. Channel spectral power for three spectral bands Alpha, Beta and
Gamma is computed. 14 x 3 = 42 features are extracted for each segment of the data. PCA is
applied to reduce the feature size. The first principal component accounts for as much of the
variability in the data as possible, and each succeeding component accounts for as much of the
remaining variability as possible [20]. We have considered only those components that contribute
to 85% (this value has been chosen after repeated trials) of the total variance for signal matching.
The power spectral density (PSD) reflects the ‘frequency content’ of the signal or the distribution
of signal power over frequency [21]. PSD is a positive real function of a frequency variable
associated with a stationary stochastic process. It is the measure of the power strength at each
frequency. In other words, it shows at which frequencies variations are strong and at which
frequencies variations are weak [18]. The unit of PSD is energy per frequency (width).
Computation of PSD can be done directly by the method of Fourier analysis or computing auto-
correlation function and then transforming it.
The Discrete Fourier transform is given by
1
( ) ( ) ( 1),N
N
i
X f x i w i
=
= −∑
Where
exp(2 ) / ,N
w i Nπ=
is the Nth root of unity. Power spectral density is given by
2
1
1
( ) | ( ) |
N
x
i
S f X f
N =
= ∑
The channel spectral power is the measure of the total power between two frequencies and is
given by:
2
1
1, 2 ( ) ,
f
xf f
f
P S f df= ∫
where (f1, f2) is the frequency band and Sx(f) is the power spectral density. The inter-hemispheric
channel spectral power differences in each spectral band are given by Pdiff = (P1 – P2) / (P1 +
P2) where P1 and P2 are the powers in different channels in the same spectral band but in the
opposite hemispheres.
3.3 Classification
The obtained feature vector is compared against a previously stored feature vector for that
subject, using Euclidean Distance for template matching. The match is considered good if the
result of the comparison is greater than the threshold value which has been set to 0.78 after
repeated trials keeping in mind the need to satisfy low False Acceptance Error (FAE) and False
Rejection Error (FRE). A proximity value of 0.78 and above is considered a good match.
3.4 Implementation
The authentication system was realized by developing an application which would lock and
unlock the screen. Initially the screen is locked and a subject’s EEG signals for two mental tasks

are recorded and stored as a reference, called the training phase. If the screen is to be unlocked,
the subject’s brain waves are recorded again and matched with the earlier stored sample. If there
is a considerable match, then the screen is unlocked, otherwise it will stay locked. The description
of the working prototype is outlined as:
• Training of the system: The brain waves of the user are recorded when he performs the
mental tasks such as meditation and math activity.
• Feature extraction: The channel spectral power in the alpha, beta and gamma spectral
bands of both the mental tasks is computed. Feature reduction technique is applied, to
reduce the dimension of the features.
• Creating user profile: These features are stored in a separate file as the user’s profile.
• Authenticating: The brain waves of the person are recorded in real time for the same set
of activities as in the training. Features are extracted from these recorded waves. Feature
reduction is performed using Principal Component Analysis and these features are
matched with the previously stored features. The feature extraction and matching part are
coded in MATLAB, while the UI part is designed and coded in C#. The MATLAB codes
are converted to Common Language Runtime (CLR) compliant library (*.dll file). These
files are then referenced in C# by means of the using statement and adding an
appropriate reference.
The User Interface diagram (Fig 4) explains the various stages and steps involved from the user’s
perspective. It depicts the different forms involved in the application for user interface. The
following steps act as a walkthrough for the application.
Step 1: The initial screen which is the main prompt screen (Fig 5) facilitates the user to perform
the lock screen, add/remove user, change account name and restore activities.
FIGURE 4: User Interface Diagram.
Step 2: We add a new user as there are no existing users initially. The training form opens
wherein we train the system for authentication. The training is based on two activities, Meditation
and Math activity. While the subject is performing these activities, the signals are recorded and
stored.

Step 3: Once the training process is complete, the user returns to the main prompt form (Fig 5).
The user can now lock the screen by clicking on the lock screen option. The login form appears
wherein user name has to be specified for unlocking the screen (Fig 6). There are 3 available
options, Unlock, Restart and Shutdown.
Step 4: When the unlock option is pressed by the user an authentication form appears. Two
activities, for which the system has been trained earlier, must be performed for authentication,
one after the other.
Step 5: If the authentication is successful then the main prompt form is displayed and the screen
is successfully unlocked, else the authentication fails and the screen remains in the locked state.
FIGURE 5: Main Prompt Window.
4. CONCLUSION
In this work, we investigate the use of brain activity for person authentication. It has been shown
in previous studies that the brain-wave pattern of every individual is unique, and that the EEG can
be used for biometric authentication. Person authentication aims to accept or reject a person
claiming an identity. We perform EEG recording, feature extraction and matching of the feature
vector with the stored feature vector, all in real time. This system seems to be the most reliable
system of authentication as it is a type “What I am” system rather than the “What I Have”
(Iris/Fingerprint scan) or “What I Know” (Password) variants of authentication system.
Additionally, this system is designed without using any type of external stimulus. This work,
however, needs more refinement such as,
i. Recording must be done in clinical conditions where there are no external interferences
(noise free environment).
ii. Training the users to perform the various mental tasks with full concentration.
iii. Handling high dimensional data.
iv. Devising a more or less perfect matching algorithm that gives 0 FAE and 0 FRE.
FIGURE 6: User login window

5. REFERENCES
[1] Palaniappan, R. (2008). “Two-stage biometric authentication method using thought activity
brain waves.” International Journal of Neural Systems, 18(01), pp. 59-66.
[2] Bengio, S., Marcel, C., Marcel, S., & Mariéthoz, J. (2002). “Confidence measures for
multimodal identity verification.” Information Fusion, 3(4), pp. 267-276.
[3] Abdullah, M. K., Subari, K. S., Loong, J. L. C., & Ahmad, N. N. (2010). “Analysis of the EEG
Signal for a Practical Biometric System.” World Academy of Science, Engineering and
Technology, 68, pp. 2067-2071.
[4] Gürkök, H., & Nijholt, A. (2012). “Brain–Computer Interfaces for Multimodal Interaction: A
Survey and Principles.” International Journal of Human-Computer Interaction, 28(5), pp. 292-307.
[5] He, C., Lv, X., & Wang, Z. J. (2009, April). ”Hashing the mAR coefficients from EEG data for
person authentication.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE
International Conference on, pp. 1445-1448.
[6] Marcel, S., & Millán, J. D. R. (2007). “Person authentication using brainwaves (EEG) and
maximum a posteriori model adaptation.” Pattern Analysis and Machine Intelligence, IEEE
Transactions on, 29(4), pp. 743-752.
[7] Majumdar, K. (2011). “Human scalp EEG processing: Various soft computing approaches.”
Applied Soft Computing, 2011(8), pp. 4433-4447.
[8] Dieckmann, U., Plankensteiner, P., & Wagner, T. (1997). “Sesam: A biometric person
identification system using sensor fusion.” Pattern recognition letters, 18(9), pp. 827-833.
[9] Xiong, N., & Svensson, P. (2002). “Multi-sensor management for information fusion: issues
and approaches.” Information fusion, 3(2), pp. 163-186.
[10] Gao, J. B., & Harris, C. J. (2002). “Some remarks on Kalman filters for the multisensor
fusion.” Information Fusion, 3(3), pp. 191-201.
[11] Tao, Q., & Veldhuis, R. (2009). “Threshold-optimized decision-level fusion and its application
to biometrics.” Pattern Recognition, 42(5), pp. 823-836.
[12] Ross, A., & Jain, A. (2003). “Information fusion in biometrics.” Pattern recognition letters,
24(13), pp. 2115-2125.
[13] Lingaraju G M, Kusuma M, Vinay K, Rakshath K, Prakash S Y, Dharini R, “Person
Authentication System Using Brain Waves as Biometric”, Conference on Evolutionary Trends in
Information Technology, Visvesvaraya Technological University, Belgaum, India, pp 47, 20-22nd
May 2011 (CETIT2011).
[14] http://www.emotiv.com/eeg/features.php
[15] Poulos, M. Rangoussi, N. Alexandris, A. Evangelou, M. (2001). “On the use of EEG features
towards person identification via neural networks.” Informatics for Health and Social Care, 26(1),
pp. 35-48.
[16] del R Millan, J., Mouriño, J., Franzé, M., Cincotti, F., Varsta, M., Heikkonen, J., & Babiloni, F.
(2002). “A local neural classifier for the recognition of EEG patterns associated to mental tasks.”
Neural Networks, IEEE Transactions on, 13(3), pp. 678-686.

[17] Fatourechi, M., Bashashati, A., Ward, R. K., & Birch, G. E. (2007). “EMG and EOG artifacts
in brain computer interface systems: A survey.” Clinical neurophysiology, 118(3), pp. 480-494.
[18] Hosni, S. M., Gadallah, M. E., Bahgat, S. F., & AbdelWahab, M. S. (2007, Nov).
“Classification of EEG signals using different feature extraction techniques for mental-task BCI.”
In Computer Engineering & Systems, 2007. ICCES'07. International Conference on (pp. 220-
226). IEEE.
[19] He, C., Lv, X., & Wang, Z. J. (2009, Apr). “Hashing the mAR coefficients from EEG data for
person authentication.” In Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE
International Conference on (pp. 1445-1448).
[20] http://www.fon.hum.uva.nl/praat/manual/Principal_component_analysis.html
[21] Saa, J. F. D., & Gutierrez, M. S. (2010). “EEG Signal Classification Using Power Spectral
Features and linear Discriminant Analysis: A Brain Computer Interface Application.”
LACCEI’2010, Innovation and Development for the Americas, Jun 1-4, 2010, Arequipa, Perú.

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