3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION 2014
Design and Simulation of Cost Effective Wireless EEG Acquisition System for Patient Monitoring Md. Kamrul Hasan1, Rushdi Zahid Rusho1, Toufiq Md. Hossain1, Tarun Kanti Ghosh2, and Mohiuddin Ahmad1 1 Dept. of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh 2
Dept. of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh Email:
[email protected],
[email protected],
[email protected],
[email protected],
[email protected]
Abstract— The motive of this paper is to design a low cost wireless EEG acquisition system for easily monitoring of the patient. Using local effort and low price employment, this system can be built which includes data acquisition, data transmission, and receiving unit which contains the patient monitoring site. The developed wireless EEG system is also suitable for the applications such as remote control of devices, rescue, etc. Realtime decoding and mobile EEG signal processing with high information transfer rate (ITR) are incorporated in the system. The specialty of the proposed research is inclusion of forth order Butterworth low pass filter which has better stability and sharper cut off with reasonable cost. Using this techniques hardware implementation is possible and GSM system can be added with hardware for long distance wireless transmission of EEG signal. The system performance is simulated by some simulation software. The proposed system is reliable, and cost is about 950 BDT or 12 USD which is reasonable. Keywords- EEG acquisition system; wireless transmission; wearable signal Monitor system; PROTEUS & PSPICE simulator.
I.
INTRODUCTION
In recent years, brain–computer interfaces (BCI) based on noninvasive scalp electroencephalography (EEG) have become an increasingly active research area [1]. EEG is a powerful non-invasive tool widely used for both biomedical diagnosis and neurophysiologic research as it provides high temporal resolution.
Figure 1. Neuron general structure and generation of electrical signal (EEG Signal) due to external stimulation.
The electroencephalogram reflects the electrical activity of the brain, which can be recorded directly from the scalp in noninvasively way using surface electrodes [2]. EEG measures the electrical potentials generated to the excitatory and inhibitory post-synaptic potentials developed by cell bodies and dendrites of pyramidal neurons [2]-[3] as shown in Fig.1. EEG signals, as one of the biological signals, are μV range (0.5 to ~ 100μV) at low frequency (0.5 to 30 ~ 40Hz). They are usually referred to as rhythms and are classified into five frequency band [1] shown in Table I. TABLE I. FREQUENCY BAND OF EEG SIGNAL
SL #
Brain waves
Frequency range (Hz)
1
Delta(δ)
1-4
2
Theta(θ)
4-8
3
Alpha(α)
8-13
4
Beta(β)
13-30
5
Gamma(γ)
36-44
Previous research on EEG machine indicated that the personal computer based EEG recorder had to communicate with the medical instrument through the computer I/O interface using RS232. But such a wired transmission is always inconvenient in mobilization. Besides, the computer usually lacks an effective program to read, analyze, and then display the EEG signals stored in conventional EEG machines. If the recorded EEG data can be treated more completely, the serviceability of the EEG acquisition system would be enhanced significantly [4]. Related works show that wireless EEG is being studied by many researchers and many wireless EEG systems have been developed recently, such as Xin Jiang et al. introduced ultrasmall two-channel EEG radio telemetry system in [5]-[8]. In this paper our effort is to design IR based EEG recording system which can be further upgraded with various wireless communication techniques [9]. The less invasive the technique the more likely it can be used in a wide range of applications [1]. Implanted electrodes provide stability of location, freedom from artifacts, and much higher signal-to-noise ratio (SNR) [1]-[2]. But one difficulty in
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3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION 2014
such a system is how to determine the locations and the number of the electrodes as well as how to keep the system stable over long periods [1]. The EEG recording electrodes and their proper function are crucial for acquiring high quality data [10]. Different types of electrodes are often used in the EEG recording systems namely 1. Disposable electrodes (gel-less, and pre-gelled types)
III.
SIMULATION RESULTS
A. Simulation of Instrumentation Amplifier Proteus 7.6 software is used to simulate instrumentation amplifier as shown in Fig. 3. The Proteus Design Suite combines schematic capture, SPICE circuit simulation, and PCB design to reduce the development time when compared with a traditional embedded design.
2. Reusable disc electrodes (gold, silver, stainless steel) 3. Headbands and electrode caps 4. Saline-based electrodes 5. Needle electrodes The raw EEG signals obtained from the electrodes have amplitudes of the order of micro volts are amplified and simulated by PROTEUS and PSPICE software. The Proteus design suite combines schematic capture SPICE circuit simulation and PCB design to make a complete electronics design system. II.
PROPOSED METHODOLOGY
The proposed methodology is briefly described as follows: The EEG signal is obtained using the conventional wet electrodes. Then it was passed through the instrumentation amplifier AD624 for amplification. Afterwards the amplified output is passed through a low pass Butterworth filter with cut off frequency 45.12 Hz. The EEG signals contain neuronal information below 100 Hz (in many applications the information lies below 30 Hz). The important EEG wave groups (alpha, beta, theta, gamma, delta, and mu) fall within 45 Hz range. Any frequency component above these frequencies can be simply removed without any loss of information [11]. From the output of filter the signal is transmitted by IR transmitter and received by photo diode receiver. The whole system is shown in Fig. 2 in which Fig. 2(a) shows the block diagram of EEG data acquisition and transmission, and Fig. 2(b) show the block diagram of receiving and control unit which may show patient monitoring system.
Figure 3. Schematic diagram of instrumentation amplifier.
Process Intelligent schematic input system (ISIS) is the heart of the PROTEUS system and is far more than just another schematics package. Industry standard SPICE3F5 simulator combined with high speed digital simulator is used. VSM signal generator of Proteus is used for variable test signal as shown in Fig. 4 in which Fig. 4(a) indicates electrode 1 & Fig. 4(b) indicates electrode 2. Filtering and amplification is validated by giving input in the order of μV with the frequency of 15 Hz. The output of the amplification is seen using animated digital oscilloscope as shown in Fig. 5 in which yellow and red signal indicate the extracted signal from electrode 1 and electrode 2 respectively, and blue signal represents the differential amplified output.
(a) (a)
(b) Figure 2. Block diagram of proposed system (a) Acquisition & Transmission. (b) Receiver & Control circuit.
(b) Figure 4. Input generator of variable amplitude and frequency wave for (a) Electrode 1 & (b) Electrode 2.
978-1-4799-5180-2/14/$31.00 ©2014 IEEE
3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION 2014
Figure 7. Poles distributing of 4th order Butterworth filter
Figure 5. Simulation result of instrumentation amplifier.
B. Simulation of 4th Order Low Pass Filter Butterworth filters are termed maximally-flat-magnituderesponse filters, optimized for gain flatness in the pass-band. The attenuation is –3 dB at the cutoff frequency. Above the cutoff frequency the attenuation is –20 dB/decade/order. The transient response of a Butterworth filter to a pulse input shows moderate overshoot and ringing. Figure. 6 shows a typical 2nd order Butterworth low pass filter. Figure 8. 4th order low-pass Butterworth filter
Figure 6. Low-Pass filter (Sallen-Key Architecture)
The transfer function of the low-pass filter is given in Eq. (1). Figure 9. Response of low-pass Butterworth filter green for input signal (500 Hz) red for 2nd order and violet for 4th order
R3 + R 4 R3 H( f ) = ( j 2πf ) 2 ( R1R1C1C 2)
(1)
⎛ ⎛ R4 ⎞ ⎞ + j 2πf ⎜⎜ R1C1 + R 2C1 + R1C 2⎜ − ⎟ ⎟⎟ ⎝ R3 ⎠ ⎠ ⎝ +1 Butterworth low pass filter is a kind of whole-pole filter, each pole is evenly distributed among the Butterworth circumference of the complex plane. Phase-angle difference of two bordering poles is 180°/n, the angle θ between the pole which is nearest to image axis and image axis is 180°/2n [3][4]. Forth order Butterworth low pass filter, the angle of θ equals 22.5 [12] as shown in Fig. 7.
A low pass 4th order Butterworth filter is designed to eliminate unwanted high frequency signals as shown in Fig. 8. A Bode plot is a graph of the transfer function of a linear, time-invariant system versus frequency, plotted with a logfrequency axis, to show the system's frequency response. The main plusses of bode plot is multiplication and addition is expressed as the addition and subtraction on the logarithmic scale. It is usually a combination of a Bode magnitude plot expressing the magnitude of the frequency response gain and a Bode phase plot expressing the frequency response phase shift.
978-1-4799-5180-2/14/$31.00 ©2014 IEEE
3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION 2014
IV.
CONCLUSSION
In this paper the architecture of IR based wireless EEG recording system has been proposed and simulated. The gain of the signal can be accomplished by instrumentation amplifier. The 4th order low-pass filter has sharp cutoff frequency and better response compared to 2nd order as shown in Fig 9 & Fig. 10. To verify the performance of the proposed system, a sinusoidal testing signal is used. It is apparent that the received signal is constructed without distortions. Better result can be expected if the quality of the electrodes is improved. To reduce electromagnetic interferences and prevent drying of conductive gel, dry electrodes can be used. The performance of the bio-signal measurement system depends on the electrodes, acquisition unit and recording conditions. By using this proposed system we can measure remote EEG Signal. Flexible and mobile BCI paradigms are possible by using the proposed method. Electrodes with higher impedance can pick up more artifacts and are mostly sensitive for movements of the electrodes. So the impedance should be reduced as much as possible as it picks up electrostatic voltages in the surrounding and electro-magnetic noise. Bluetooth, Wi-Fi, Li-Fi, GSM etc. can be used instead of IR based transmitter and receiver to extend range.
(a)
REFERENCES
(b) Figure 10. Frequency response of low-pass Butterworth filter for (a) 4th order (b) 2nd order
C. IR Based Transmitter And Receiver Infrared light is electromagnetic radiation with longer wavelengths than those of visible light, extending from the nominal red edge of the visible spectrum at 700 nanometers (nm) to 1 mm.
[1]
[2]
[3]
[4]
[5]
[6]
Figure 11. Schematic of (a) transmitter (b) Receiver
[7]
This range of wavelengths corresponds to a frequency range of approximately 430 THz down to 300 GHz. Most of the thermal radiation emitted by objects near room temperature is infrared. The proposed IR transmitter and receiver circuit is shown in Fig. 11.
[8]
[9]
M. K. Hasan, R. Z. Rusho, and M. Ahmad “A Direct Noninvasive Brain Interface with Computer Based On Steady-State Visual-Evoked Potential (SSVEP) With High Transfer Rates” International Conference on Advances in Electrical Engineering (ICAEE), 2013, Dkaka, Bangladesh. H. Saadi, M. Ferroukhi and M. Attari “Development of wireless high immunity EEG recording system” 2011 International Conference on Electronic Devices, Systems and Applications (ICEDSA). C. Moulin “ Contribution à l’étude et à la réalisation d’unsystème électronique de mesure et excitation de tissue nerveux à matrices microélectrodes”, Thèse de doctorat, Lyon, 2006. R. Lin, R. G. Lee, C. L. Tseng, Y. F. Wu, J. A. Jiang “Design And Implementation Of Wirelessmulti-Channel Eeg Recording System And Study Of Eeg Clustering Method” Biomed Eng Appl Basis Comm, 2006(December); 18: 276-283. X. Jiang and X. Wang, “Development of Ultra-Small Two Channel System of EEG Radio Telemetry”, Proceedings of 1st International Conference on Neural Interface and Control Proceedings, Wuhan, china, pp.60-63, 2005. H. Chen, D. Ye, J. Lee, “Development of a Portable EEG Monitoring System based on WLAN”, Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control, London, UK, 15-17 April 2007. S. P. R. Balakrishnan “Multichannel Wireless EEG Recording system” International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012. D. Zhang, X. Gao, D. C. Yu ”A Wireless Electroencephalogram Telemetry Recording System for Roaming Animals” Proceedings of 2011 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices Sydney, Australia, December 14-16, 2011. H. Chen, D. Ye, J. Lee “Development of a Portable EEG Monitoring System based on WLAN”, Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control, London, UK, 15-17 April 2007.
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3rd INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION 2014
[10] S. I. Arman, A. Ahmed, and A. Syed “ Cost-Effective EEG Signal Acquisition and Recording System ” International Journal of Bioscience, Biochemistry and Bioinformatics, Vol. 2, No. 5, September 2012. [11] O. J. Baztarrica, “EEG Signal Classification for Brain Computer Interface Applications.” s.l.: Ecole polytechnique federale De lausanne, 2002.
[12] L. Zhongshen “Design and Analysis of Improved Butterworth Low Pass Filter” The Eighth International Conference on Electronic Measurement and Instruments ICEMI’2007.
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