Starfast-a Wireless Wearable EEG Biometric System based on the ENOBIO Sensor

STARFAST: a Wireless Wearable EEG Biometric System based on the ENOBIO Sensor H.S.PAVAN KUMAR

C.ABDUL KHALEEK

Madina engineering college

Madina engineering college

ABSTRACT—we present a wearable,

Rate (FAR) of 0.82%. The obtained

wireless biometry system based on the new

performance measures improve the results

ENOBIO

of similar systems presented in earlier work.

4-

channel

electrophysiology

recording device developed at Star lab.

I. INTRODUCTION

Features

from

The present paper introduces a method to

and

authenticate people from their physiological

electrocardiogram (ECG) recordings have

activity, concretely the combination of ECG

proven to be unique enough between

and EEG data. We call this system

subjects for biometric applications. We

STARFAST (STAR Fast Authentication

show here that biometry based on these

bio-Scanner

recordings offers a novel way to robustly

modalities are already being exploited

authenticate or identify subjects. In this

commercially for person authentication:

paper, we present a rapid and unobtrusive

voice recognition, face recognition and

authentication method that only uses 2

fingerprint recognition are among the more

frontal electrodes and a wrist worn electrode

common modalities nowadays. But other

referenced to another one placed at the ear

types of biometrics are being studied as

lobe. Moreover, the system makes use of a

well: ADN analysis, keystroke, gait, palm

multistage

extracted

electroencephalogram

Test).

Several

biometric

architecture,

which

print, ear shape, hand geometry, vein

improved

system

patterns, iris, retina and written signature

performance. The performance analysis of

[33]. Although these different techniques for

the system presented in this paper stems

authentication exist nowadays, they present

from an experiment with 416 test trials,

some problems. Typical biometric traits,

where an Equal Error Rate (EER) of 0% is

such as fingerprint, voice and retina, are not

obtained after the EEG and ECG modalities

universal, and can be subject to physical

fusion and using a complex boundary

damage (dry skin, scars, loss of voice...). In

decision. If a lineal boundary decision is

fact, it is estimated that 2-3% of the

used we obtain a True Acceptance Rate

population is missing the feature that is

(TAR) of 97.9% and a False Acceptance

required for authentication, or that the

demonstrates

fusion

(EEG)

to

provided biometric sample is of poor

problem of entity authentication in body

quality. Furthermore, these systems are

area sensor network (BASN) for m-Health.

subject of attacks such as presenting a

Physiological signals detected by biomedical

registered deceased person, dismembered

sensors have dual functions: (1) for a

body part or introduction of fake biometric

specific medical application, and (2) for

samples. It also compares the performance

sensors in the same BASN to recognize each

of the system with the Active Two system

other by biometrics. A feasibility study of

from Biosemi. The Active Two system is

proposed entity authentication scheme was

state of the art commercial equipment that is

carried out on 12 healthy individuals, each

proven to have very good performance and

with 2 channels of photoplethysmogram

has been used in many studies. A list of

(PPG) captured simultaneously at different

some works can be found in [1]. So far 3

parts of the body. The beat-to-beat heartbeat

demonstration

been

interval is used as a biometric characteristic

developed using the system. EOG based

to generate identity of the individual. The

Human Computer Interface (HCI), EEG and

results of statistical analysis suggest that it is

ECG based Biometry for Authentication

a possible biometric feature for the entity

(presented at pHealth as a poster) and an

authentication of BASN.

applications

have

EEG based Sleepiness Prediction system for

Advantages

Since

every

drivers. In all cases the applications are

living and functional person has a recordable

designed around the same 4 channel system.

EEG/ECG signal, the EEG/ECG feature is

ENOBIO has been developed with the help

universal. Moreover brain or heart damage

of the SENSATION FP6 IP 507231.

is something that rarely occurs, so it seems

With the evolution of m-Health, an

to be quite invariant across time. Finally it

increasing number of biomedical sensors

seems very difficult to fake an EEG/ECG

will be worn on or implanted in an a

signature

individual in the future for the monitoring,

biometric system.

diagnosis, and treatment of diseases. For the

system

optimization of resources, it is therefore

characteristics:

necessary to investigate how to interconnect

friendliness, fast operation and low cost. The

these sensors in a wireless body area

perfect biometric trait should have the

network, wherein security of private data

following characteristics: very low intra-

transmission is always a major concern. This

subject variability, very high inter-subject

paper proposes a novel solution to tackle the

variability, very high stability over time and

or

to

should

attack

an

EEG/ECG

An ideal biometric present

100%

the

following

reliability,

user

universality. In the next section we show the

four channels plus electrical reference can

general

be recorded. The hardware is power with a

architecture

performance

of

the

and

the

system

global

we

have

developed.

rechargeable

Li-Ion

battery

for

long

duration. The communication of ENOBIO with the Laptop is done via ZigBee to USB

SYSTEM

DESCRIPTION

AND

SPECIFICATIONS:

Ag/AgCl or optionally dry. We can see in

ENOBIO is a wearable, wireless, 4channel,

all-digital

receiver. The electrodes can be active

electrophysiology

Figure 1 ENOBIO recording EEG data and transmitting them wirelessly to the laptop to

recording system that has been optimized for

be processed for an on-line application.

dry electrodes. The system has been

METHODS AND RESULTS:

designed in a modular way to allow

In this paper, we present a rapid and

researchers to build systems tailored to their

unobtrusive

needs. Robust, snap-in cables of any length

method that only uses 2 frontal electrodes

allow any combination of EEG, EOG and

(for EEG recording) with another electrode

ECG in a single wearable system without

placed

loss of signal quality. ENOBIO employs

recording)and all 3 referenced to another

three fundamental enabling aspects to bring

one placed at the right earlobe. Moreover the

electrophysiology into daily life:

system makes use of a multi-stage fusion

- Active digital electrodes digitize

on

authentication/identification

the

left

wrist

(for

ECG

architecture, which has been demonstrated

the signal on site in order to reduce

to

environmental noise.

EEG/ECG recording device is ENOBIO [23,

improve

system

performance.

The

24, 32, 35], a product developed at - Modular design allows users to customize

STARLAB BARCELONA SL. It is a

a single system for various applications

wireless 4 channel (plus the common mode) device with active electrodes. It is therefore

- Wireless communication allows wearable

quite unobtrusive, fast and easy to place.

recording of data away from the lab or

Even though ENOBIO can work in dry

controlled environments. The precision of

mode, in this study conductive gel has been

the signal that can be acquired with

used (see fig. 1).

ENOBIO is 1-3 uVpp to 1 mV from 0.1 to 100 Hz, and the output is a digital signal at 256 Samples/s per channel, 16 bit. Up to

decision.

The

enrolment

and

the

authentication test follow a common path until the feature extraction. Then the red arrows indicates the enrolment path and the green ones the authentication path. Briefly, the preprocessing module segments the recorded takes into 4 seconds epochs. From Figure1: ENOBIO sensor embedded in a

each one of those epochs we extract 5

cap. We can see the 4 channel inputs (red

different

pins). In the picture, only two channels are

coefficients, fast Fourier transform, mutual

plugged. The data are transmitted wirelessly

information, coherence and cross correlation

to the laptop. The general schema used is

[10, 11, 12, 19, 20, 21]. The classifier used

described below: Fig. 2 General scheme of

in both the signature extraction module and

the method. The data acquisition module is

in the recognition module is the Fisher

the software that controls the ENOBIO

Discriminant Analysis [13], with 4 different

sensor in order to capture the raw data.

discriminant functions

Remember that 4 channels are recorded: 2

linear, quadratic and diagonal quadratic).

EEG channels placed in the forehead, 1

During the enrolment process, we need four

ECG channel placed in the left wrist and 1

3-minutes takes, and these are used to select

electrode placed in the right earlobe for

the best combinations of features and

referencing the data. At this point the data is

classifiers, and then to train the classifiers

separate in EEG data and ECG data and sent

for each subject. This new feature is what

to two parallel but different biometric

we call ‘personal classifier’ approach, and it

modules for EEG and ECG. (the difference

improves the performance of the system

is not shown in the scheme for simplicity).

considerably. The authentication test takes

For the signature extraction module, four 3-

are 1-minute long. The preprocessing and

minutes

the feature extraction are equivalent to the

takes

are

needed.

Once

the

features:

auto-regression

(linear,

diagonal

signatures are extracted, they are both stored

enrolment case.

in the database for further retrieval when an

loads the trained classifiers for each

authentication process takes place. Then the

modality

recognition

independent score is then provided for each.

module

provides

an

(ECG

The recognition module and

authentication score for both modalities and

INITIAL

SYSTEM

finally the fusion module provides the final

BENCHMARKING:

EEG)

and

an

For the signal analysis we compared the signal recorded with ENOBIO and Biosemi simultaneously. The electrodes of every system where placed in Fp1 of 1 cm between them. We analyzed the signal referenced to left ear lobe. We recorded spontaneous EEG, 1 minute with eyes open And 1 minute with the eyes closed for 2 subjects, using wet Ag/AgCl electrodes for

Figure 3: Spectral density of the signal

both systems. Figures 2 to 5 show the power

recorded with ENOBIO and Biosemi in fp1

spectral density of the signals recorded with

referenced to left ear lobe. Sample was 1

ENOBIO (blue line) and Biosemi (red line).

minute of spontaneous EEG with eyes close.

We can see a very good match in the

DEMONSTRATION

spectrums of both systems. Just a small peak

APLICATIONS:

of 50 Hz appears for ENOBIO for subject 1.

Tree different applications have been built on ENOBIO system. In this section we make a fast overview of them. A EOG based Human

Computer

Interface.

Star

lab

developed a system to track eye movements. When we look at a scenario, we focus our objects of interest changing our view with fast eye movements. These movements are detected by the system and their amplitude And direction extracted from the EOG Figure 2: Power spectral density of the

signal. The signal is acquired by ENOBIO

signal recorded with ENOBIO and Biosemi

and sent wirelessly to the ENOBIO server

in fp1 referenced to left ear lobe. Sample

installed in a laptop. This raw signal can be

was 1 minute of spontaneous EEG with eyes

sent via TCP/IP to any application, in that

open.

case to the EOG HCI system. The application processes the data in real time and outputs a vector that represents the movement on the screen. The

System is set on a cap as shown in Figures 1

himself), 350 impostor situations (when an

and 8.

enrolled subject claims to be another subject from the database) and 16 intruder situations (when a subject who is not enrolled in the system claims to be a subject belonging to the database). Once the EEG and the ECG biometrics results are fused, using a complex boundary decision (dotted line 2 in

Figure 4: EOG-HMI system. 3 electrodes are placed on the forehead; the 4th one and the electrical reference are placed in the ear lobes with a clip. Figure 7 shows a graphical representation of the output of a movement.

fig. 3), we can obtain an ideal performance, that is True Acceptance Rate (TAR) = 100% and False Acceptance Rate (FAR) = 0%. If a linear boundary decision is used (line 1 in fig. 3), we obtain a TAR = 97.9% and a FAR = 0.82%. The results are summarized in table I. Bidimensional decision space. An ordinate represent the ECG probabilities and abscises the EEG probabilities. Red crosses represent impostor/intruder cases and green crosses represent legal cases. Two decision functions are represented this system has been tested as well to validate the initial

Figure 5: The vector of the picture

state of users, and has been proved sensitive

represents the eye gaze movement of a user

enough to detect it. If a subject has suffered

of the EOG Human Computer Interface. The

from sleep deprivation [28], alcohol intake

absolute error in the prediction for the

[26, 27] or drug ingestion when passing an

movements is 5, 9 % with a standard

authentication

deviation of 6.6 for the X axe, and 9, 9%

performance decreases. This fact provides

with a standard deviation of 13, 1 for the Y

evidence that such a system is able to detect

axe. Then the fusion module [22] provides a

not only the identity of a subject but his state

final

as well.

decision

authentication.

about In

order

the to

subject test

the

performance of our system we use 48 legal situations (when a subject claims to be

TABLE I

test,

the

authentication

validate in a continuous way that the person supposed to be tele-present in an audiovisual interactive space is actually the person that is supposed to be. This could facilitate the personalization of the reaction of the virtual

CONCLUSION

AND

FUTURE

WORK: These

results

show

that

the

authentication of people from physiologic data can be achieved using techniques of machine learning. Concretely it shows that the fusion of two (or more) independent biometric

modules

increases

the

performance of the system by applying a fusion stage after obtaining the biometric scores. This result shows that processing the different physiological modalities separately on

different

processing

modules,

and

introducing a data fusion step, the resulting performance can be increased. Applying a very similar approach, we could easily adapt the system to do emotion recognition [29, 30, and 34] from physiological data, or develop a Brain Computer Interface, just starting from different ground truth data. From our point of view, this easy to extend feature of our system is the more interesting part of our study along with the ‘personal classifier’

approach

which

improves

considerably the performance of the system. The system described could have different applications for Virtual Reality. It can

environment [31], or secure interactions that guarantee the authenticity of the person behind. Although the system described here was oriented towards person identification, its performance has been significantly modified by introducing physiological data obtained in altered states such as the ones resulting from sleep deprivation. This indicates that such a system could be used to extract dynamically changing, such as physiological activity related to mood or to intense cognitive activity. For example, it could be used to extract the features described (sleep deprivation, alcohol intake), but also information about basic emotions (see, for example, [34]). The dynamic extraction of such features could be used to evaluate the response of people in virtual environments, as well as adapt the behavior of such environments to the information extracted dynamically. The advantage that such a system would present compared to currently existing approaches is that –by having built a decisional system that is completely modular- we could estimate each class independently of the other classes obtained, which would allow us to have a modular approach to define the interaction

between the human and the machine. Finally

[4] Mohammad G. et al. (2006) Person

we would like to highlight the usefulness of

identification by using AR model

using a single system for user state

For EEG signals. Proc. 9th International

monitoring

Conference on Bioengineering

and

user

personalization.

Assuming that the system is worn anyway

Technology (ICBT2006), Czech Republic,5

for physiological feedback, this same system

pp.

can personalize your experience because it

[5]

knows who you are without any interaction

electroencephalogram as a biometric.

required by the user.

Proc. Canadian Conf. On Electrical and

ENOBIO is an electrophysiology recording

Computer Engineering, pp.

wireless system. The quality of the signal is

1363-1366.

proven to be as good as the signal that can

[6] Poulos M. et al. (1998) Person

be acquires with state of the art wired

identification via the EEG using

equipment. Different applications can be

Computational

built on the ENOBIO system. We presented

Proceedings of the Ninth

a biometric application, a Human Machine

European Signal Processing, EUSIPCO’98,

Interface and a Sleepiness prediction system.

Rhodes, Greece.

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