STARFAST: a Wireless Wearable EEG Biometric System based on the ENOBIO Sensor
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.doc   Starfast-a Wireless Wearable EEG Biometric System based on the ENOBIO Sensor.doc (Size: 670.5 KB / Downloads: 173)

STARFAST: a Wireless Wearable EEG Biometric System based on the ENOBIO Sensor

Presented By:
H.S.PAVAN KUMAR C.ABDUL KHALEEK
Madina engineering college Madina engineering college


ABSTRACT”
we present a wearable, wireless biometry system based on the new ENOBIO 4- channel electrophysiology recording device developed at Star lab. Features extracted from electroencephalogram (EEG) and electrocardiogram (ECG) recordings have proven to be unique enough between subjects for biometric applications. We show here that biometry based on these recordings offers a novel way to robustly authenticate or identify subjects. In this paper, we present a rapid and unobtrusive authentication method that only uses 2 frontal electrodes and a wrist worn electrode referenced to another one placed at the ear lobe. Moreover, the system makes use of a multistage fusion architecture, which demonstrates to improved system performance. The performance analysis of the system presented in this paper stems from an experiment with 416 test trials, where an Equal Error Rate (EER) of 0% is obtained after the EEG and ECG modalities fusion and using a complex boundary decision. If a lineal boundary decision is used we obtain a True Acceptance Rate (TAR) of 97.9% and a False Acceptance Rate (FAR) of 0.82%. The obtained performance measures improve the results of similar systems presented in earlier work.
I. INTRODUCTION

The present paper introduces a method to authenticate people from their physiological activity, concretely the combination of ECG and EEG data. We call this system STARFAST (STAR Fast Authentication bio-Scanner Test). Several biometric modalities are already being exploited commercially for person authentication: voice recognition, face recognition and fingerprint recognition are among the more common modalities nowadays. But other types of biometrics are being studied as well: ADN analysis, keystroke, gait, palm print, ear shape, hand geometry, vein patterns, iris, retina and written signature [33]. Although these different techniques for authentication exist nowadays, they present some problems. Typical biometric traits, such as fingerprint, voice and retina, are not universal, and can be subject to physical damage (dry skin, scars, loss of voice...). In fact, it is estimated that 2-3% of the population is missing the feature that is required for authentication, or that the provided biometric sample is of poor quality. Furthermore, these systems are subject of attacks such as presenting a registered deceased person, dismembered body part or introduction of fake biometric samples. It also compares the performance of the system with the Active Two system from Biosemi. The Active Two system is state of the art commercial equipment that is proven to have very good performance and has been used in many studies. A list of some works can be found in [1]. So far 3 demonstration applications have been developed using the system. EOG based Human Computer Interface (HCI), EEG and ECG based Biometry for Authentication (presented at pHealth as a poster) and an EEG based Sleepiness Prediction system for drivers. In all cases the applications are designed around the same 4 channel system. ENOBIO has been developed with the help of the SENSATION FP6 IP 507231.
With the evolution of m-Health, an increasing number of biomedical sensors will be worn on or implanted in an a individual in the future for the monitoring, diagnosis, and treatment of diseases. For the optimization of resources, it is therefore necessary to investigate how to interconnect these sensors in a wireless body area network, wherein security of private data transmission is always a major concern. This paper proposes a novel solution to tackle the problem of entity authentication in body area sensor network (BASN) for m-Health. Physiological signals detected by biomedical sensors have dual functions: (1) for a specific medical application, and (2) for sensors in the same BASN to recognize each other by biometrics. A feasibility study of proposed entity authentication scheme was carried out on 12 healthy individuals, each with 2 channels of photoplethysmogram (PPG) captured simultaneously at different parts of the body. The beat-to-beat heartbeat interval is used as a biometric characteristic to generate identity of the individual. The results of statistical analysis suggest that it is a possible biometric feature for the entity authentication of BASN. Advantages Since every living and functional person has a recordable EEG/ECG signal, the EEG/ECG feature is universal. Moreover brain or heart damage is something that rarely occurs, so it seems to be quite invariant across time. Finally it seems very difficult to fake an EEG/ECG signature or to attack an EEG/ECG biometric system. An ideal biometric system should present the following characteristics: 100% reliability, user friendliness, fast operation and low cost. The perfect biometric trait should have the following characteristics: very low intra-subject variability, very high inter-subject variability, very high stability over time and universality. In the next section we show the general architecture and the global performance of the system we have developed.
SYSTEM DESCRIPTION AND SPECIFICATIONS:
ENOBIO is a wearable, wireless, 4-channel, all-digital electrophysiology recording system that has been optimized for dry electrodes. The system has been designed in a modular way to allow researchers to build systems tailored to their needs. Robust, snap-in cables of any length allow any combination of EEG, EOG and ECG in a single wearable system without loss of signal quality. ENOBIO employs three fundamental enabling aspects to bring electrophysiology into daily life: - Active digital electrodes digitize the signal on site in order to reduce environmental noise.
- Modular design allows users to customize a single system for various applications
- Wireless communication allows wearable recording of data away from the lab or controlled environments. The precision of the signal that can be acquired with 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 four channels plus electrical reference can be recorded. The hardware is power with a rechargeable Li-Ion battery for long duration. The communication of ENOBIO with the Laptop is done via ZigBee to USB receiver. The electrodes can be active Ag/AgCl or optionally dry. We can see in Figure 1 ENOBIO recording EEG data and transmitting them wirelessly to the laptop to be processed for an on-line application.
METHODS AND RESULTS:
In this paper, we present a rapid and unobtrusive authentication/identification method that only uses 2 frontal electrodes (for EEG recording) with another electrode placed on the left wrist (for ECG recording)and all 3 referenced to another one placed at the right earlobe. Moreover the system makes use of a multi-stage fusion architecture, which has been demonstrated to improve system performance. The EEG/ECG recording device is ENOBIO [23, 24, 32, 35], a product developed at STARLAB BARCELONA SL. It is a wireless 4 channel (plus the common mode) device with active electrodes. It is therefore quite unobtrusive, fast and easy to place. Even though ENOBIO can work in dry mode, in this study conductive gel has been used (see fig. 1).


Figure1: ENOBIO sensor embedded in a cap. We can see the 4 channel inputs (red pins). In the picture, only two channels are plugged. The data are transmitted wirelessly to the laptop. The general schema used is described below: Fig. 2 General scheme of the method. The data acquisition module is the software that controls the ENOBIO sensor in order to capture the raw data. Remember that 4 channels are recorded: 2 EEG channels placed in the forehead, 1 ECG channel placed in the left wrist and 1 electrode placed in the right earlobe for referencing the data. At this point the data is separate in EEG data and ECG data and sent to two parallel but different biometric modules for EEG and ECG. (the difference is not shown in the scheme for simplicity). For the signature extraction module, four 3-minutes takes are needed. Once the signatures are extracted, they are both stored in the database for further retrieval when an authentication process takes place. Then the recognition module provides an authentication score for both modalities and finally the fusion module provides the final 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 each one of those epochs we extract 5 different features: auto-regression coefficients, fast Fourier transform, mutual information, coherence and cross correlation [10, 11, 12, 19, 20, 21]. The classifier used in both the signature extraction module and in the recognition module is the Fisher Discriminant Analysis [13], with 4 different discriminant functions (linear, diagonal linear, quadratic and diagonal quadratic). During the enrolment process, we need four 3-minutes takes, and these are used to select the best combinations of features and classifiers, and then to train the classifiers for each subject. This new feature is what we call Ëœpersonal classifierâ„¢ approach, and it improves the performance of the system considerably. The authentication test takes are 1-minute long. The preprocessing and the feature extraction are equivalent to the enrolment case. The recognition module loads the trained classifiers for each modality (ECG and EEG) and an independent score is then provided for each.
INITIAL SYSTEM BENCHMARKING:
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 both systems. Figures 2 to 5 show the power spectral density of the signals recorded with ENOBIO (blue line) and Biosemi (red line). We can see a very good match in the spectrums of both systems. Just a small peak of 50 Hz appears for ENOBIO for subject 1.

Figure 2: Power spectral density of the signal recorded with ENOBIO and Biosemi in fp1 referenced to left ear lobe. Sample was 1 minute of spontaneous EEG with eyes open.

Figure 3: Spectral density of the signal recorded with ENOBIO and Biosemi in fp1 referenced to left ear lobe. Sample was 1 minute of spontaneous EEG with eyes close.
DEMONSTRATION APLICATIONS:
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 signal. The signal is acquired by ENOBIO and sent wirelessly to the ENOBIO server installed in a laptop. This raw signal can be sent via TCP/IP to any application, in that 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 and 8.

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.

Figure 5: The vector of the picture represents the eye gaze movement of a user of the EOG Human Computer Interface. The absolute error in the prediction for the movements is 5, 9 % with a standard deviation of 6.6 for the X axe, and 9, 9% with a standard deviation of 13, 1 for the Y axe. Then the fusion module [22] provides a final decision about the subject authentication. In order to test the performance of our system we use 48 legal situations (when a subject claims to be himself), 350 impostor situations (when an 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 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 state of users, and has been proved sensitive enough to detect it. If a subject has suffered from sleep deprivation [28], alcohol intake [26, 27] or drug ingestion when passing an authentication test, the authentication performance decreases. This fact provides evidence that such a system is able to detect not only the identity of a subject but his state as well.
TABLE I

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 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 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 we would like to highlight the usefulness of using a single system for user state monitoring and user personalization. Assuming that the system is worn anyway for physiological feedback, this same system can personalize your experience because it knows who you are without any interaction required by the user.
ENOBIO is an electrophysiology recording wireless system. The quality of the signal is proven to be as good as the signal that can be acquires with state of the art wired equipment. Different applications can be built on the ENOBIO system. We presented a biometric application, a Human Machine Interface and a Sleepiness prediction system.
REFERENCES
[
1] Eischen S., Luctritz J. and Polish J. (1995) Spectral analysis of EEG
From Families. Biological Psycholology, Vol. 41, pp. 61-68.
[2] Hazarika N., Tsoi A. and Sergejew A. (1997) nonlinear considerations
in EEG signal Classification. IEEE Transactions on signal processing,
Vol. 45, pp.829-836.
[3] Marcel S., Mill J. (2005) Person authentication using brainwaves
(EEG) and maximum a posteriori model adaptation. IDIAP Research
Report 05-81, 11 pp.
[4] Mohammad G. et al. (2006) Person identification by using AR model
For EEG signals. Proc. 9th International Conference on Bioengineering
Technology (ICBT2006), Czech Republic,5 pp.
[5] Para jape R. et al.(2001)The electroencephalogram as a biometric.
Proc. Canadian Conf. On Electrical and Computer Engineering, pp.
1363-1366.
[6] Poulos M. et al. (1998) Person identification via the EEG using
Computational geometry algorithms. Proceedings of the Ninth
European Signal Processing, EUSIPCOâ„¢98, Rhodes, Greece.
September1998, pp. 2125-2128.
[7] Poulos M. et al. (1999) parametric person identification from EEG
Using computational geometry. Proc. 6th International Conference on
Electronics, Circuits and Systems (ICECSâ„¢99),v. 2, pp. 1005-1008.
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.doc   STAR Fast Authentication bio-Scanner Test.doc (Size: 2.13 MB / Downloads: 105)
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This article is presented by:Y.G.MANJUSHA

ABSTRACT

This topic introduces a method to authenticate people from their physiological activity, concretely the combination of ECG and EEG data.
We call this system STARFAST (STAR Fast Authentication bio-Scanner Test).
Several biometric modalities are already being exploited commercially for person authentication: voice recognition, face recognition and are among the more common Modalities now a day. But other types of fingerprint recognition biometrics are being studied as well: ADN analysis, keystroke, gait, palm print, ear shape, and hand Geometry, vein patterns, iris, retina and written signature .Although these different techniques for authentication exist now days, they present some problems. Typical biometric traits, such as fingerprint, voice and retina, are not universal, and can be subject to physical damage (dry skin, scars, loss of voice) In fact, it is estimated that 2-3% of the population is missing the feature that is required for authentication, or that the provided biometric sample is of poor quality. Furthermore, these systems are subject of attacks such as presenting a registered deceased person, dismembered body part or introduction of fake biometric samples.
New types of Biometrics, such as electroencephalography (EEG) and electrocardiography (ECG), are based on physiological signals, rather than more traditional biological traits. These have some advantages: Since every living and functional person has a recordable EEG/ECG signal, the EEG/ECG feature is universal. Moreover brain or heart damage is something that rarely occurs, so it seems to be quite invariant across time. Finally it seems very difficult to fake an EEG/ECG signature or to attack an EEG/ECG biometric system.
An ideal biometric system should present the following characteristics: 100% reliability, user friendliness, fast operation and low cost. The perfect biometric trait should have the following characteristics: very low intra-subject variability, very high inter-subject variability, very high stability over time and universality. In the next section we show the general architecture and the global performance of the system we have developed.


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.doc   Starfast-a Wireless Wearable EEG Biometric System based on the ENOBIO Sensor.doc (Size: 670 KB / Downloads: 63)
STARFAST: a Wireless Wearable EEG Biometric System based on the ENOBIO Sensor


ABSTRACT
—we present a wearable, wireless biometry system based on the new ENOBIO 4- channel electrophysiology recording device developed at Star lab. Features extracted from electroencephalogram (EEG) and electrocardiogram (ECG) recordings have proven to be unique enough between subjects for biometric applications. We show here that biometry based on these recordings offers a novel way to robustly authenticate or identify subjects. In this paper, we present a rapid and unobtrusive authentication method that only uses 2 frontal electrodes and a wrist worn electrode referenced to another one placed at the ear lobe. Moreover, the system makes use of a multistage fusion architecture, which demonstrates to improved system performance. The performance analysis of the system presented in this paper stems from an experiment with 416 test trials, where an Equal Error Rate (EER) of 0% is obtained after the EEG and ECG modalities fusion and using a complex boundary decision. If a lineal boundary decision is used we obtain a True Acceptance Rate (TAR) of 97.9% and a False Acceptance Rate (FAR) of 0.82%. The obtained performance measures improve the results of similar systems presented in earlier work.
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