DESIGNING OF A LOW COST BIOSIGNAL ACQUISITION FOR CONTROLLING REHABILITATION SYSTEM

JOBIN JOSE

211BM1209

NATIONAL INSTITUTE OF TECHNOLOGY, ROURKELA

Abstract

A large section of our society suffers from one or other kind of disabilities due to accidents,

neurological disorders etc. These disabilities force these patients to depend on their family

members or care-givers for day-to-day activities including mobility, communication with the

environment, controlling the house hold equipment etc. recent advancement in the biomedical

field able to produce neural linkage with Computers with various biomedical signals which can

be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples

include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG).

Such approaches are extremely valuable to physically disabled persons. With the help of these

biosignals they can interact with a computer and other accessories. This review deals with

different methods that can be used to improve the performance of rehabilitation aids for motor

impaired persons.

Keywords: EOG, EEG, EMG, rehabilitation aid ,BCI, HCI

1. Introduction and Objective

According to national institute of health there are around 2.7 lakh persons in the country who are

affected by paraplegia or quadriplegia. A common thought haunted by most of these people is

that they are a burden to their families and society because for every day to day action for them

to perform they need to depend on family even for transport from one place to other. Most of the

time the life led by these peoples are miserable. Different techniques by rehabilitation

engineering help to improve the life of these people. An ideal rehabilitation aid is such that it

collect information from the surrounding, analyze those information, convey those information

to user and finally receive commands from user. With the advance in current research in image

and signal processing we can provide artificial intelligence to system that can interpret the

information automatically. The use of these rehabitation aids aid the disabled person to do their

da -to-day activities without depending on others.

Out of all the rehabilitation techniques HCI (Human Computer Interphase) becomes the newest

and the most effective technique. Lot of researches are going in the field of HCI. Main aim of

HCI systems is converting signals generated by the humans or gestures made by the humans are

converted to some keystrokes or mouse movements. In HCI both biosignals and non biosignals

are used for control. Biosignals used are EMGs, EEGs and EOGs.

1.2 Introduction to EOG signal approach

Metabolic activities in the cornea region are higher than the retinal region. This leads to the development of a potential difference between these two parts. Usually cornea maintains a voltage of +0.40 to +1.0 millivolts higher than the retina. [1][4]. EOG signal is based on electrical potential difference between the cornea and retina when eye movement is realized. The amplitude of this signal ranges between [50, 3500] µV and its frequency components go from 0 to 100Hz. An eye movement is related to a rise/fall of EOG signal amplitude.[2] Two voltage levels, “low” and “high”, can be defined in order to distinguish between a deliberate movement and a non-deliberate one. Both, the “low” level and the “high” level, can be identified by a threshold value which is defined by equations.

The merits of EOG systems are as follows:

1. Acquisition of EOG signal is easy..

2. EOG signals can cover wide range of view

3. EOG signals are very fast. Thus real-time implementation is possible. [3]

This signal is a bio potential measurable around the eye - either between the top and bottom of

the eye (the vertical EOG), or between the two sides of the eye (the horizontal

EOG). The EOG amplitude varies as the eyeball rotates within the head, and thus can be used to

determine horizontal and vertical eye movements.[5]

In Figure 2-1, positive or negative pulses will be generated when the eyes rolling upward or downward. The amplitude of pulse will be increased with the increment of rolling angle, and the width of the positive (negative) pulse is proportional to the duration of the eyeball rolling process

Fig 2-1 Eye movement and the corresponding waveform

Fig 2.2 Electrode placement for EOG data collection [2]

(h: horizontal, v: vertical, r: reference)

In our HCI system, three to five electrodes are employed to attain the EOG signals. Figure 2-2

shows the electrode placement. EOG can be measured either from 3 electrodes or from 5

electrode. In 3 electrode technique only 1&2 with reference electrode can also be used. 1 & 4

are for detecting vertical movement and 2 & 3 for detecting horizontal movement. The electrode

5 is for reference. Blink detection is by separate algorithm based on EOG signals[6]

Outputs of electrodes are amplified and filtered this is our front end electronics after that using

A/D convertor signal is digitized and this completes our Acquisition of Signal part. Now we

combine all acquire signals from all electrodes and send them via RF interface for future Pre and

post processing of signals and finally Eye movements and Eye blink events are extracted and

sent as commands to drive cursor on screen which is part of application part, buttons one the

cursors are clicked using eye moments and eye blink and designated action is completed but

application circuit.

3. Materials and Work plan

3.1 Materials

The ICs AD620 and OP07 were procured from Analog devices, Norwood, USA and Fairchild,

South Portland, USA, respectively. Disposable pre-gelled electrodes for EMG signal acquisition

was obtained from BPL, Bangalore, INDIA. Ni-MH rechargeable batteries (9 V) were obtained

from UNIROS, Bristol, UK. Data acquisition device (USB-6008) was procured from Advantech

corporation, Taiwan. Arduino Leonardo microcontroller board was procured from Arduino

corporation Italy. The capacitors, resistors motors and other parts were procured from local

market.

3.2 Work Plan

Fig 3.1 Basic Block Diagram of Eye based architecture

Fig 3.2 and 3.3 Vertical and Horizontal EOG acquisition

In our HCI system, three to five electrodes are employed to attain the EOG signals. Figure 3.2

AND 3.3 shows the electrode placement. EOG can be measured either from 3 electrodes or from

5 electrode. In 3 electrode technique only horizontal electrodes with reference electrode can also

be used. In 5 electrode both vertical movement and horizontal movements are detected. The

electrode 5 is for reference. Blink detection is by separate algorithm based on EOG signals

The noise present in the signal can be removed by using filters. The baseline wandering also

can be removed by filters.

Separate algorithms are used for identifying a blink and eye movement. They are identified by

the width of waveform and height of waves. For eye movements width of the pulse will be more

but amplitude is low. For blink waveform amplitude of the pulse will be more but duration of of

pulse is low.by using separate algoriths this can be identified.

This step is proceed by bio signal to control signal generation. Separate eye movements and

duration between individual eye movement can be formulated to produce individual control

signals.

By using these control signals different rehabilitation aids can be accessed and controlled.

4. Result and Discussion

4.1 EOG signal acquisition system

In general, the EOG signal amplitude and bandwidth varies in the range of 50, 3500 µV [3].

USB-4704 is 12-bit analog-to-digital converter. Hence, care has to be taken to design a

biopotential which will be able to amplify the signal in such a way that the digitization of the

EOG signals result in minimum quantization error. To ascertain this, the gain of the bio potential

amplifier should be adjusted so that 0.01 mV EOG signal is amplified above 1.4 mV. Also, the

Characteristic of AD620 suggests that as the gain of bio potential amplifier is increased there is a

subsequent increase in the common-mode rejection ratio (CMRR). Taking the above facts into

consideration, the biopotential amplifier was designed with a gain of 1000.[6][9]

AD620 instrumentation amplifier was chosen as the front end amplifier due to its high CMRR

at high gain. Differential amplifier in contained in the instrumentation amplifier allows

subtraction of the common or unwanted signal from the actual signal but the perfect subtraction

is never done. The high the common mode rejection ratio indicates the better subtraction and the

CMRR of the AD620 at a gain of 1000 is about 130 dB, is more than sufficient for this project .A

single external resistor sets the gain of AD620 from 1 to 10000, the resistor is connected between

the 1st and 8th legs of the opamp. The gain can be calculated by the following equation.

G=1+49.3 ΩRg

Where Rg is the the resistor is connected between the 1st and 8th legs of the opamp.

where Rg = 2R.

when R = 22Ω the Rg = 44 and Gain will be approximately 1100

Fig 4.1 Pre amplifier with Driven Right Leg design.

To increase the CMRR the body reference ciruit is designed using Driven Right Leg design.[5]

To prevent the noise that have been amplified by the preamplifier circuit, a band pass filter

was applied to the output of preamplfier. Filter also helps to sink any DC current that might

cause bias for the signal.After the initial signal is amplified by the front end amplifier, the signal

is passed through a Sallen-Key Butterworth high pass filter with a .05Hz cut-of frequency

rejecting all the motion artifacts but at the same time letting pass the lower end of the frequency

band of interest.The high-pass filter is followed by a a Sallen-Key 2 pole Butterworth low-pass

filter with a 35Hz cut-of frequency in order to band pass the signal.[8] The gain of the band-pass

filter is 1 as the gain of the high-pass filter and the gain of the low-pass filter are both 1.

Fig 4.2 Band Pass Filter Consisting of a High Pass and a Low Pass Filter

Fig

4.3

Original EOG waveform

corresponding to eye movements obtained from DAQ

Fig 4.4 Original EOG signal obtained from Blinking.

5. Work Remaining

An efficient and low cost Data Acquisition system for detecting EOG is devoped. Remaining

work includes the devolepment of rehabilitation techniques and generation of control signals

from generated EOG. Which will be completed during next semester.

6. Expected Result and Conclusion

A system used to detect eye movement based on the EOG signal is proposed. So the system objective is to detect when a movement of eyes is realized and the route described. . In this project, events generated for the system when an eye movement is done can be used to handle computer applications causing as less fatigue as possible to the user. This allows handicapped

people are able to access the computer in an easy and comfortable form. So, in the future, we will use the EOG signal as communication interface to handle an application based on augmentative and alternative communication. Also, we will detect the stress and fatigue of user in order to use these results in ambient living application.

6. References

1. Malik Arslan, Ahmad Jehanzeb. “Retina Based Mouse Control (RBMC)”, World Academy of Science, Engineering and Technology 31 2007; pp. 318-322

2. Ahsan Md. R, Ibrahimy I Muhammad, Khalifa O Othman.“EMG Signal Classification for Human Computer Interaction: A Review”. European Journal of Scientific Research ISSN 1450-216X Vol.33 No.3 (2009), pp.480-501

3. Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., and Vaughan T.M., 2002.“Brain-computer interfaces for communication and control,” Electroenceph. Clin. Neurophysiol., vol. 113, no. 6, pp. 767–791.

4. Dornhege G., Millan J., Hinterberger T., McFarland D., and Muller Eds. K.-R., 2007.“TowardBrain Computer Interfacing. Cambridge,MA: MIT Press.

5. Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., and Vaughan T.M., 2002.“Brain-computer interfaces for communication and control,” Electroenceph. Clin. Neurophysiol., vol. 113, no. 6, pp. 767–791.

6. Curran E. A. and Strokes M. J., 2003. “Learning to control brain activity: A review of the production and control of EEG components for driving brain– computer interface (BCI) systems,” Brain Cognition, vol. 51, pp. 326– 336.

7. Ebrahimi T., Vesin J. M., and Garcia v, 2003. “Brain–computer interface in multimedia communication,” IEEE Signal Process. Mag., vol. 20, no. 1, pp. 14–24.

8. Fisch B. J., 1999. Fisch & Spehlmann’s EEG Primer. Amsterdam, The Netherlands: Elsevier.

9. Gopi E.S., Sylvester Vijay R., Rangarajan V., Nataraj L., 2006. “Brain Computer InterfaceAnalysis using Wavelet Transforms and Auto Regressive Coefficients,” Electrical and

Computer Engineering, 2006. ICECE '06. International Conference on, pp. 169 – 172.