my seminar(mobile device for electronic eye gesture recognition)

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M. Kirbiš and I. Kramberger: Mobile Device for Electronic Eye Gesture Recognition Contributed Paper Manuscript received September 2, 2009 0098 3063/09/$20.00 © 2009 IEEE 2127 Mobile Device for Electronic Eye Gesture Recognition Matej Kirbiš and Iztok Kramberger, Member, IEEE Abstract Based on the fact that there are many challenging cases of infirm persons, who are able to control only their eye muscles, a low-cost mobile device for electronic eye gesture recognition has been designed as a human- machine interface, which enables the control of different applications and home appliances by user’s eye gestures by the IR and Bluetooth wireless technology. In this paper the embedded system design of the device is presented in detail, including hardware and software design, modes of operation, and used methods for the eye gesture recognition. Beside these, measurements of the used differential amplifier and the achieved eye gesture recognition efficiency are presented within the test results. Furthermore, a newly designed adjustable head mounted EOG acquisition device with the permanent surface electrodes is proposed. Index Terms electrooculogram, disabled people, eye gesture, blink, microcontroller. I. INTRODUCTION Eye movements are proven the most frequent of all human movements [1]. Eye movement research is of great interest in the study of neuroscience and psychiatry as well as ergonomics, advertising and design. Since eye movements can be controlled volitionally to some degree, and tracked by the modern technology with great speed and precision they can now be used as a powerful input device and have many practical applications in human-computer interactions. Many methods have been adopted to test eye movements [2-4]. Carlos H. et al. compared the characteristics of traditional eye gazing techniques (Table 1) [5]. TABLE 1 CHARACTERISTICS OF TRADITIONAL EYE TRACKING TECHNIQUES Technique Accurac y Comments Contact lens 1’ Intrusive but fast and accurate EOG (a) Simple and low cost IROG (b) 2’ Head mounted, limbus tracking Pupil tracking Camera based, hard to detect Image-based 0.5-2° Camera based, requires training (a) Electrooculography. (b) Infrared oculography. This work was supported in part by the European Union – European Social Fund. M. Kirbiš is with the Research Department, Company Astron d.o.o., Jadranska cesta 27, 2000 Maribor, Slovenia (e-mail: [email protected]). I. Kramberger is with the Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia (e-mail: [email protected]). There are many challenging cases concerning disabled people like those infirm persons that are able to control only the muscles of their eyes [6]. From this point of view, the aim of this project was to design a low cost embedded mobile device with a friendly user interface for disabled people, which would feature human-machine interaction by measuring the EOG potentials and further analysis for eye gesture recognition. To be able to communicate with the rest of the world the device is equipped with widely used Bluetooth and IR (Infra Red) communication technologies (Fig. 1). EOG potential is a resting potential of the retina and it linearly depends on the eye gazes in area from -30 to 30 degrees. The position of the eye gaze can be measured with proper placement of the electrodes. In our case we are using three pairs of electrodes (one pair for measuring horizontal EOG potential and other two pairs for measuring vertical EOG potential – separately for each eye). The weakness of this method is the fact that we have to place electrodes on the user’s face. We are using surface mount medical electrodes rather then special deep ones (needles) and therefore measurement of the EOG potential is not entirely accurate as it is influenced by the activity of the muscles nearby. On the other hand, the influence of nearby muscles activity can be used to determinate other eye gestures like blinking. Fig. 1. Features of communication. Despite of that, EOG signaling obtained by surface electrodes is adequate for further analysis in order to recognize user’s eye gestures therefore a big advantage of this method is the possibility to build a low cost device for electronic eye gesture recognition. With features of communication the device enables user to control numerous peripheral devices and home appliances, and beside this it can be used as a medical device for different medical applications and purposes.

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Page 1: My Seminar(Mobile Device for Electronic Eye Gesture Recognition)

M. Kirbiš and I. Kramberger: Mobile Device for Electronic Eye Gesture Recognition

Contributed Paper Manuscript received September 2, 2009 0098 3063/09/$20.00 © 2009 IEEE

2127

Mobile Device for Electronic Eye Gesture Recognition Matej Kirbiš and Iztok Kramberger, Member, IEEE

Abstract — Based on the fact that there are many

challenging cases of infirm persons, who are able to control only their eye muscles, a low-cost mobile device for electronic eye gesture recognition has been designed as a human-machine interface, which enables the control of different applications and home appliances by user’s eye gestures by the IR and Bluetooth wireless technology. In this paper the embedded system design of the device is presented in detail, including hardware and software design, modes of operation, and used methods for the eye gesture recognition. Beside these, measurements of the used differential amplifier and the achieved eye gesture recognition efficiency are presented within the test results. Furthermore, a newly designed adjustable head mounted EOG acquisition device with the permanent surface electrodes is proposed. Index Terms — electrooculogram, disabled people, eye gesture, blink, microcontroller.

I. INTRODUCTION Eye movements are proven the most frequent of all human

movements [1]. Eye movement research is of great interest in the study of neuroscience and psychiatry as well as ergonomics, advertising and design. Since eye movements can be controlled volitionally to some degree, and tracked by the modern technology with great speed and precision they can now be used as a powerful input device and have many practical applications in human-computer interactions. Many methods have been adopted to test eye movements [2-4]. Carlos H. et al. compared the characteristics of traditional eye gazing techniques (Table 1) [5].

TABLE 1 CHARACTERISTICS OF TRADITIONAL EYE TRACKING TECHNIQUES

Technique Accuracy

Comments

Contact lens 1’ Intrusive but fast and accurate EOG(a) 2° Simple and low cost IROG(b) 2’ Head mounted, limbus tracking Pupil tracking

1° Camera based, hard to detect

Image-based 0.5-2° Camera based, requires training

(a) Electrooculography. (b) Infrared oculography.

This work was supported in part by the European Union – European Social Fund.

M. Kirbiš is with the Research Department, Company Astron d.o.o., Jadranska cesta 27, 2000 Maribor, Slovenia (e-mail: [email protected]).

I. Kramberger is with the Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia (e-mail: [email protected]).

There are many challenging cases concerning disabled people like those infirm persons that are able to control only the muscles of their eyes [6]. From this point of view, the aim of this project was to design a low cost embedded mobile device with a friendly user interface for disabled people, which would feature human-machine interaction by measuring the EOG potentials and further analysis for eye gesture recognition.

To be able to communicate with the rest of the world the device is equipped with widely used Bluetooth and IR (Infra Red) communication technologies (Fig. 1).

EOG potential is a resting potential of the retina and it linearly depends on the eye gazes in area from -30 to 30 degrees. The position of the eye gaze can be measured with proper placement of the electrodes. In our case we are using three pairs of electrodes (one pair for measuring horizontal EOG potential and other two pairs for measuring vertical EOG potential – separately for each eye).

The weakness of this method is the fact that we have to place electrodes on the user’s face. We are using surface mount medical electrodes rather then special deep ones (needles) and therefore measurement of the EOG potential is not entirely accurate as it is influenced by the activity of the muscles nearby. On the other hand, the influence of nearby muscles activity can be used to determinate other eye gestures like blinking.

Fig. 1. Features of communication.

Despite of that, EOG signaling obtained by surface

electrodes is adequate for further analysis in order to recognize user’s eye gestures therefore a big advantage of this method is the possibility to build a low cost device for electronic eye gesture recognition. With features of communication the device enables user to control numerous peripheral devices and home appliances, and beside this it can be used as a medical device for different medical applications and purposes.

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IEEE Transactions on Consumer Electronics, Vol. 55, No. 4, NOVEMBER 2009 2128

II. HARDWARE DESCRIPTION In this section the embedded system’s hardware structure is

described. Figure 2 shows a block diagram of the presented device. Beside this, a prototype of the presented device (fig. 3) and the future design of the device’s casing (fig. 4) are also shown.

Fig. 2. Block diagram.

All three EOG potentials are measured as differential signals, which are normally in range of 50 µV to 3500 µV, and therefore they are firstly amplified by differential amplifiers with their gain set to 60 dB. Two stage amplifiers have been designed in order to obtain high gain with cut-off frequency of 50 Hz and to convert differential signaling into common mode.

Fig. 3. Prototyped device.

To achieve proper rejection of common mode potentials on user’s face a feedback as a reference signal is provided over an additional electrode, which is placed on the user’s forehead. The reference signal represents an inverted and

integrated sum of common mode signals from all differential amplifiers with a constant integration time of 2.2 seconds.

At the next processing stage all three amplified signals are acquired and converted into digital form by a 14-bit analog to digital converter (ADC).

By the use of serial peripheral interface (SPI) the ADC is connected to a 32-bit microcontroller. The sample rate of EOG signal acquiring is set to 100 Hz for each channel while all three channels are read out simultaneously. Microcontroller that runs at 66 MHz represents the core of the device and is furthermore connected to Bluetooth and IR communication units. Bluetooth connectivity is achieved with a Bluetooth serial module, which is connected to the microcontroller over the standard embedded serial interface. Connection can be established to any Bluetooth equipped device that features connectivity via serial service.

Fig. 4. Future design of the device’s casing.

On the other hand, one way IR communication is

implemented achieved with an external power stage and an embedded pulse width modulation unit (PWM). The IR interface enables control of home appliances, automation systems, and similar devices like Hi-Fi, TV sets, air conditioning, etc.

The embedded universal serial bus (USB) is currently used only for first time system software programming and further upgrades. Furthermore, power supply provided by the USB is used for charging the embedded single cell Li-Ion battery, which supplies the device by a 5 V charge pump and a 3.3 V linear low drop regulator.

The power management unit is used to switch the power of the device with a single button and is implemented with an tiny 8-bit microcontroller. The button is also used to choose between two primary modes of operation where audible-visual feedback is provided by a LED indicator and a beeper.

The mobile device for electronic eye gesture recognition is a medical device, which requires extra safety precautions and is therefore designed to meet all the required safety regulations. In particular, the connection of facial electrodes must meet special requirements. For this reason touch proof medical connectors have been used.

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III. MODES OF OPERATION The presented device can operate in two different primary

modes: - application interface mode and - standalone mode. The user can choose between these two modes by a short

push to the front-panel button described in the previous section.

A. Application interface mode As already mentioned, the device can be connected to any

device that supports Bluetooth serial service. In this mode an application interface is exposed at device side that enables the control of operation and data transmitting. By default, the baud rate of Bluetooth serial interface is set to 19200 bps. Furthermore, the device can be put into two different sub-modes via the application interface mode:

- gesture recognition sub-mode and - raw sub-mode. Simple commands used by the application interface are

shown within the Table 2.

TABLE 2 SUPPORTED COMMANDS

ASCII code Command description ’I’ Return the device name ’G’ Set device into gesture sub-mode ’R’ Set device into raw sub-mode ’T’ Start transmission ’P’ Stop transmission ’B’ Return

When device is operating in gesture sub-mode, it tries to

detect changes in gaze and furthermore eye gestures from the acquired EOG signaling in real time by implication of embedded pattern recognition. Successfully recognized eye gestures represent the return information, which is sent back to the control application via exposed serial service. Within the table 3 the return information in gesture sub-mode is shown:

TABLE 3

RETURN INFORMATION IN GESTURE SUB-MODE ASCII code Recognized gesture

‘L’ Left gesture ‘R’ Right gesture ‘U’ Up gesture ‘D’ Down gesture ‘1’ Left blink* ‘2’ Right blink*

* Blinking is not an EOG signal but the influence of muscles nearby.

Within raw sub-mode the pattern recognition is omitted and the return information that is transmitted via Bluetooth serial service to the control application is based on previously encoded packets of the acquired EOG signaling. Figure 5 shows the encoded packet formation of the acquired EOG signaling.

Fig. 5. Encoded packet of EOG signaling. The raw sub-mode of the device can be useful for medical

purposes and applications like EOG signal recording or for implication of further and more advanced EOG signal processing that is performed by the application on connected device or personal computer. The sample rate provided in this mode is 100 Hz for each EOG channel and therefore the encoded packet is sent each 10 ms.

B. Standalone mode Within the standalone mode the device operates as a

standalone unit in the sense of a widely used IR remote control. The successfully recognized eye gestures are mapped into a previously chosen set of pre-learned commands and transmitted over the one-way IR communication unit in order to trigger pre-programmed actions on the external device. There can be many different sets of pre-learned commands stored within the embedded EEPROM to control different home appliances. The procedure to choose from different sets of commands to control different appliances has not been implemented yet and therefore the device is currently able to control only one pre-programmed appliance at the time.

IV. EMBEDDED SOFTWARE

In this section the embedded system’s software is presented. Within figure 6 the embedded software layers are shown.

Fig. 6. Embedded software layers.

The presented firmware can be divided into three different layers:

- communication layer, - middleware layer and - hardware abstraction layer.

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IEEE Transactions on Consumer Electronics, Vol. 55, No. 4, NOVEMBER 2009 2130

The highest layer of the firmware supervises the communication. At this layer Bluetooth and IR device high level functions are realized.

At second middleware layer data handling and pattern recognition functions are realized. Figure 7 represents the data flow within the middleware layer.

Fig. 7. Dataflow within the middleware layer.

The middleware layer functionality depends on the

operational mode of the device. When raw sub-mode is chosen the captured data representing the acquired EOG signaling is encoded and transmitted instantly. On the other hand, the pattern recognition is performed at this layer if the device is placed into gesture sub-mode. Acquired EOG signals that are provided by the ADC are firstly filtered with single-pole high pass IIR filter in order to remove the DC components present in the signals. Applied IIR filter can be described with the equation (1):

( ) ( ) ( )1-nyany 0 +⋅= nx , (1)

where x(n) represents the input signal, y(n) the output

signal, and a0 the IIR filter coefficient. Value of the filter coefficient a0 is set to 0.1 as the required cutoff frequency of the high pass filter is 1 Hz and the sample rate is 100 Hz.

Afterwards pattern recognition is performed on acquired data in real time to examine the presence of possible eye gestures in up/down and left/right directions.

At the lowest hardware abstraction layer of device’s firmware the data acquiring, timing, and in-circuit communication functions are realized. This layer takes care of the low-level functions like SPI communication, Bluetooth configuration, IR modulation and similar.

V. PATTERN RECOGNITION In this section the pattern recognition is described as a part

of the embedded EOG signal analysis. Figure 8 shows a typical EOG signal, which occurs by horizontal eye movements from initial (center) gaze to absolute gaze in left direction (left gesture) and back to initial (right gesture), and opposite from initial gaze to absolute gaze in right direction (right gesture) and back to initial (left gesture).

It can be seen (fig. 8) that we have do deal with the relatively slow signals and the information about the changes in gaze and presence of eye gestures can be obtained from the shape of the signal.

Fig. 8. EOG signal of typical eye gestures.

When designing a low cost device with a low power

consumption microcontroller we have typically limited signal processing power available. In regards to that and considering previously mentioned findings the cross correlation method was chosen for pattern recognition.

Cross correlation represents one of the basic methods for pattern recognition [7]. The time discrete cross correlation function can be expressed by the following equation (2):

( ) ( ) ( )∑∞

−∞=

−⋅=n

xy lnynxlr , (2)

where x(n) and y(n) represent the input signals of the cross

correlation function and l denotes the size of the window for comparison.

The result of the cross correlation can be normalized to the interval of [-1, 1] by the following equation (3):

( ) ( )( ) ( )00 yyxx

xyxx rr

lrl

⋅=ρ , (3)

where rxy represents the cross correlation value between the

input signals, and rxx, ryy the cross correlation values between the inputs signals them self (autocorrelation).

A. Eye gesture recognition As the cross correlation method can be used to determinate

the similarity between two signals x(n), y(n), we have defined a reference signal y(n), which characterizes a typical eye gesture from initial gaze to another absolute gaze direction (fig. 9).

The interpolated reference signal model was obtained from a pre-recorded and high pass filtered EOG signal (fig. 10). It can be seen (fig. 10) that typical eye gesture from initial gaze

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to another absolute gaze position can last up to about 400 ms. Consequently, as the sample rate of the EOG signals is 100 Hz, the value of 40 samples was chosen for the cross correlation window size l.

Fig. 9. Typical eye gesture reference signal model.

The normalized cross correlation values between the input

signals x(n)H, x(n)V and the reference signal model y(n) are computed in real time for each input sample, where x(n)H and x(n)V denote the acquired horizontal and vertical EOG potentials. In other words, the two corresponding cross correlation windows for horizontal and vertical direction are moved sample vise, while for each horizontal and vertical sample a new cross correlation value is obtained. The same reference signal model y(n) is used for recognition of horizontal (left/right) and vertical (up/down) eye gestures.

Fig. 10. High pass filtered EOG signal of typical eye gestures.

Furthermore, in order to detect eye gestures, the obtained

normalized cross correlation values for both vertical and horizontal samples are compared to the predefined threshold values. In the moment, when the cross correlation value reaches a certain threshold level, an eye gesture is recognized and valid until the next instance occurs. The threshold value of 0.8 for left and -0.8 for right eye gesture respectively have been chosen. The applied threshold values have been defined experimentally by the analysis of the cross correlation function applied to several prerecorded EOG signals.

B. Blink gesture recognition As already mentioned, the eye blinking is not an EOG

signal but the influence of the nearby muscles activity, which is present in vertical EOG signaling when the surface mount electrodes are used. Compared to the typical eye movement, the influence of the nearby muscles activity at blink gesture occurrence creates a large signal change within the vertical EOG signalling in a short time (fig. 11).

Fig. 11. Comparison of gaze and blink gestures.

It can be seen (fig. 11) that the eye blink gesture occurs

within a time frame of 100 ms, and furthermore the signal change present in vertical EOG signalling almost saturates the amplifier.

Therefore the eyes blink gesture recognition is implemented as detection of an event, at which the acquired vertical EOG signalling changes from negative to positive threshold value within a time frame of 10 samples. The values of -2 V and +2 V have been chosen for negative and positive threshold.

As the device is equipped with two separate vertical EOG channels, the blink gesture recognition is performed on both channels simultaneously in real time, while the blink gestures are recognized for each eye separately.

VI. EXPERIMENTAL RESULTS At this section some typical characteristics and parameters

of the device’s electronic circuits and the experimental results of the device usage are presented.

TABLE 4

DIFFERENTIAL AMPLIFIER MEASUREMENTS Parameter Value

SNR 80 dB CMRR 77 dB

The analog part of the device is based on a high gain two

stage three channel differential amplifier. The measured values of achieved signal to noise ratio (SNR) and common mode rejection ratio (CMRR) of the implemented amplifier are given within the table 4.

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IEEE Transactions on Consumer Electronics, Vol. 55, No. 4, NOVEMBER 2009 2132

It can be seen that the values of SNR and CMRR are adequate to the applied 14-bit analog to digital conversion, which is provided by the ADC unit.

It can also be seen that the gain of almost 60 dB has been achieved by the differential amplifier (fig. 12).

A gain drop of about 3 dB can be seen within the low frequency to DC range of the gain versus frequency characteristic, which in fact is the consequence of the measurement method used, as the input signal was provided to the highly sensitive differential inputs of the amplifier over a voltage transformer.

Fig. 12. Gain vs. frequency characteristic of the differential amplifier. When the device is fully operating, the power consumption

reaches up to about 260 mW (depends on the Bluetooth activity), and within the standby mode it drops down to 0.7 mW.

Fig. 13. A screenshot captured from the test application.

A special test application that runs on a personal computer

has been designed to measure the efficiency of the embedded eye gesture recognition by means of true/false recognition rate. The test application can run in two different modes: learning mode and measurement mode. In learning mode user navigates a ball displayed on the screen by his eye gestures while in measurement mode the application itself moves the ball in different directions randomly and simultaneously given moves are compared to the current user’s eye gestures, which

are recognized by the device. Before each application randomly moves, an arrow is displayed at the side of the ball to notify the user about the direction of the next move (fig. 12). In such a case the user can prepare him to make a proper eye gesture in the given direction.

First of all, the mobile device has to be paired with the personal computer via Bluetooth and the connection over the serial service has to be established between the test application and the application interface of the mobile device.

Ten first time users (3 female, 7 male) have been tested by the provided test application, where the test procedure has been performed in four stages. At the first stage (learning mode) users could have tested the application for 5 minutes by navigating the ball by their eye gestures. Within further three stages the eye gesture recognition rate has been measured for horizontal, vertical and both directions separately by 50 moves in random directions for each particular stage. Test results of the average recognition rate of ten users for particular stage are presented within the table 5.

TABLE 5 TEST RESULTS OF EYE GESTURE RECOGNITION Test type Average rate [%]

Horizontal eye gestures 90 Vertical eye gestures 78.5

Eye gestures in both directions 82

Test results show that in average the device’s eye recognition performs below 90 percent, in particular for the eye gestures in both directions.

On the other hand, test results for particular users can show a recognition rate up to 94 percent (4 users) and also up to 100 percent (1 user). It has been found that the proper placement of the surface mount electrodes has a large influence on the eye gesture recognition rate.

Fig. 14. Future design of an adjustable head mounted EOG sensor with embedded permanent electrodes.

Consequently, design of an adjustable head mounted EOG sensor with embedded permanent surface mode electrodes has been initiated to solve the problems with proper placement of surface electrodes (fig. 14). Beside this, such a device extension also simplifies the placement of the electrodes to the user’s head.

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The eyes blink gesture recognition efficiency has not been tested separately as in general it shows a true/false recognition rate up to 100 percent for left, right and both eyes.

Fig. 15. Tridimensional model of the head mounted EOG sensor.

Furthermore, it has been found that eye gestures can also be

recognized at user’s head movements while the user is gazing into a fixed point. Therefore the future head mounted EOG sensor (fig. 15) will be equipped with an additional 3-axes MEMS accelerometer in order to be able to detect head movement.

VII. CONCLUSION In this paper a mobile device for electronic eye gesture

recognition is presented. It has been shown that the device is equipped with wireless communication technologies and is battery powered to assure high level of mobility. Beside this, design of the device is low-cost and light-weight. The hardware implementation of the device follows medical requirements with the use of touch proof connectors for facial electrodes and proper design of the differential amplifier input stages. Beside this, the device can operate within an application mode and also as a standalone device and features a friendly user interface. Within the standalone mode the device emulates a programmable IR remote control, while the eye gestures are mapped into commands to trigger actions on different appliances. On the other hand, application interface mode can be used in connection with other Bluetooth equipped devices and also for medical and scientific purposes.

Further work is required in order to optimize the eye gesture recognition rate in a way that the device could be used more widely. One way to achieve this is the implementation of the adjustable head mounted EOG sensor, which will assure proper placement of facial electrodes and also simplify the procedure of mounting the electrodes to the user’s head.

VIII. REFERENCES [1] B. Bridgeman, “Conscious vs. unconscious processes: The case of

vision”, Theory and Psychology, vol. 2, No. 1, pp.73-88, 1992. [2] Q. Ji, H. Wechsler, A. Duchowski, M. Flickner, “Special issue: eye

detection and tracking”, Computer Vision and Image Understanding, Vol. 98, No. 1, pp. 1-3, 2005.

[3] S. Kawato, N. Tetsutani, “Detection and tracking of eyes for gaze-camera control”, The 15th International Conference on Vision Interface, Calgary, May 27-29, 2002..

[4] J. Kim, “A simple pupil-independent method for recording eye movements in rodents using video”, Journal of Neuroscience Methods, Vol. 138, No. 1-2, pp. 165-171, 2004.

[5] C. Morimoto, M. Mimica, “Eye gaze tracking techniques for interactive applications”, Computer Vision and Image Understanding, Volume 98, No. 1, pp 4-24, 2005.

[6] Q. Ding, K. Tong, G. Li, “Development of an EOG (Electro-Oculography) Based Human-Computer Interface”, Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.

[7] N. Phuong, “Digital Correlation”, Vietnam OpenCourseWare module, Jul 2008.

Matej Kirbiš vas born in 1983 and received B.Sc. degree in electrical engineering from the University of Maribor, Slovenia in 2007. He is currently working toward his PhD degree in electrical engineering as a young researcher, financed by the European Union social fund. Since 2008 his is employed at the Research Department of the Company Astron. His research interests include digital signal processing for human-computer interfaces

and brain computer interfaces.

Iztok Kramberger (M’99-VM’09) was born in 1973 and received B. Sc, M. Sc, and PhD degrees from the University of Maribor, Slovenia, in 1997, 2001, and 2003, respectively, all in electrical engineering. Since 2009 he has been a valued IEEE member for 10 years. Since 2009 he heads the Laboratory for digital and information systems at the Faculty of Electrical

Engineering and Computer Science of University of Maribor. Since 2006 he has been the Vice-president of the board for electronic communications of the Republic of Slovenia and the Vice-president of the Board for Science and Technology of Chamber of Craft and Small Business of Slovenia. His research interests include computer vision, augmented and virtual reality, human-computer interfaces, brain computer interfaces, and hardware-accelerated image processing. In the opinion of industry he received the awards Researcher of the year 2006 and Researcher of the year 2008 from Technocenter of the University of Maribor, Slovenia.