SlideShare a Scribd company logo
1 of 10
Download to read offline
International INTERNATIONALCommunication Engineering & Technology (IJECET), ISSN 0976 –
               Journal of Electronics and JOURNAL OF ELECTRONICS AND
 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME
       COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 3, Issue 3, October- December (2012), pp. 177-186
                                                                             IJECET
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2012): 3.5930 (Calculated by GISI)                  ©IAEME
www.jifactor.com




               A REVIEW OF EEG RECORDING TECHNIQUES
                               Imteyaz Ahmad1, F Ansari2, U.K. Dey3
                     1
                       Dept of ECE , 2Dept of Electrical Engg. , 3Dept of Mining Engg.
                                      BIT Sindri, Dhanbad, Jharkhand
               1
                 Corresponding Author Tel.-+91-0326-2245671 Fax no.-+91-0326-2350729
                Email address for correspondence author-imtiazahmadbitsindri@gmail.com

 ABSTRACT
 EEG is a collaborative tool to diagnosis brain function and disease. Modern interpretation of
 EEG origin rest with knowledge of basic neuronal electrochemical process. The EEG is
 composed of electrical rhythms and transient discharges which are distinguished by location,
 frequency, amplitude, form, periodicity and functional properties. The low-level EEG Signal
 from the brain as recorded is amplified and converted to a digital signal for further processing.
 EEG machines have a notch filter sharply tuned at 50Hz so as to eliminate main frequency
 interference. Reading has been taken in international 10-20 Electrode system for a normal
 person. EEG signal voltage amplitudes ranges 1 to 100 µv peak to peak at low frequencies(1
 to 50 Hz) at the surface of scalp. In biofeedback training the ratio of amplitude of alpha to
 theta waves need to be increased.

 Keyword: Electroencephalogram(EEG), biofeedback, notch filter.
 INTRODUCTION

 The human brain contains approximately 100 billion nerve cells called neurons. Neurons have
 the amazing ability to gather and transmit electrochemical signals. The Neurons have 3 basic
 parts, a cell body which has the necessary cells components, Axon which is like a long cable to
 carry nerve impulse and finally the Dendrites which is the nerve ending branches that connects to
 other cells to allow electrical transfers between cells. The generation of EEG potentials requires
 a neural source close to the inside surface of the skull that is coherent, which means all the
 neurons must be aligned similarly and act together electrically. Pyramidal cells in the center of
 the cerebral cortex are the major source of EEG potentials. The dendrites will receive excitatory
 or inhibitory inputs from surrounding neurons and axons. When the dendrites receive an impulse
 or input by an ion such as Na+ enters them (become active), current flows into and out of these
 dendritic processes and the cell body. The cell to dendrite relationship is therefore one of a

                                                 177
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME

constantly shifting current dipole, and variations in orientation and strength of the dipole produce
wave like fluctuations in a volume conductor. When the sum of electrical activity is negative
relative to cell, the cell is depolarized and quite excitable. When it is positive, the cell is
hyperpolarized and less excitable. EEG potentials on the scalp are usually no more than 150uV
peak to peak. The brain frequencies depended on the degree of activity of the cerebral cortex. For
example, the waves change between states of wakefulness and sleep. Much of the time, the brain
waves are irregular and no general pattern can be observed. Yet at other times, distinct patterns
do occur. Some of these are characterized to be abnormalities of the brain such as epilepsy.
Generally there are four wave groups (alpha, beta, theta, and delta). The EEG rhythm and
waveforms are varied by the position of electrode placements on certain parts of the brain (fig.1).
Alpha wave occurs at a frequency between 7.5 and 13Hz. The alpha waves are produced when a
person is in a conscious, relaxed state with eyes closed; the activity is suppressed when the eyes
are open. The amplitude of the alpha rhythm is largest and intensely occurs in the occipital
region and can be best recorded at parietal and frontal regions of the scalp. Beta waves normally
occur in the frequency range of 14-30Hz and sometimes even as high as 50Hz for intense
activity. Beta waves activities are present when people are alert or anxious, with their eyes open.
Theta potentials are large amplitude, low frequency between 3.5 and 7.5Hz waves. Theta is
abnormal in alert adults but seen during sleep, and small children. Theta waves occur mainly in
the parietal and temporal region. Delta waves have the largest amplitudes and the lowest
frequency in less than 3.5Hz. It is normal rhythm for infants less than one year old and in adults
in deep sleep. This wave can thus occur solely within the cortex, independent of the activities in
lower regions of the brain.




Electroencephalography (EEG) is the measurement of electrical activity produced by the brain as
recorded from electrodes placed on the scalp. Just as the activity in a computer can be
understood on multiple levels from the activity of individual transistors to the function of
applications, so can the electrical activity of the brain be described on the relatively small to
relatively large scales. At one end are action potentials in a single axon or currents with a single
dendrite of a single neurons at the other end is the activity measured by the EEG which
aggregates the electric voltage field from million of neurons .so called scalp EEG is collected
from tens to hundreds of electrodes positioned on different location at the surface of the head
EEG signal ( in the range of milli volts) are amplified and digitalized for later processing. The
data measured by the scalp EEG are used for clinical and research purposes. In neurology the
main diagnostic application of EEG is for epilepsy by the this technique is also used to
investigate many other pathologies such as sleep related disordered ,sensory deficits, brain
tumors, etc. In cognitive neuroscience, EEG is used to investigate the neural correlates of mental

                                                178
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME

activity from low level perceptual and motor processes to higher order cognition (attention,
memory, reading etc.) EEG electrodes transfer ionic currents from cerebral tissue into electrical
currents used in EEG preamplifiers. The electrical characteristic are determined primarily by the
type of metal used. silver-silver chloride (Ag-Ag cl) is commonly found in electrode discs.
Five types of electrode are used:
1.Sclap- silver pads, discs, or cup; stainless steel rods; and chlorides silver wires.
2. Sphenoidal- alternating insulated silver and bare wire and chloride tip inserted through muscle
tissue by a needle.
3. Nasopharyngeal- silver rod with ball at the tip inserted through the nostrils.
4.Eltrocorticographic- cotton wicks soaked in saline solution that rest on the brain surface.
5. Intercerebral- sheaves of Teflon-coated gold or platinum wire cut a various distance from the
sheaf tip and used to electrically stimulate the brain.

BASIC RECORDING SYSTEM
Referential: The potential difference is measured between an active electrode and an inactive
reference electrode




                  Figure 2 (a) Referential method
Bipolar: The potential difference is measured between two active electrode




                    Figure 2(b) Bipolar method
EEG is a representation of the electrical activity of the brain. .The technique involve following:
1.Biopotential peak up- Cranial or cerebral surface transducer electrodes
2.EEG signal conditioning-Transducer output amplification and filtering (.1 to 100Hz)
3.EEG signal recording-Signal displayed on graphic recorder or CRT
4. EEG Signal analysis- visual or computer interpretation of resting EEG
EEG is a collaborative tool is diagnosis brain function and disease. Many Physician and
neurologist view EEG signal as interest artifacts but confess that then are not certain of the signal
                                                179
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October December (2012), © IAEME
                                                         October-

origins. In fact ,until recently EEG waveform were originally thought to be a summation of
action potential of neurons as they made their was to cranial surface later idea reflects
stimulation associated by diverse neuron .
 Modern interpretation of EEG origin rest with knowledge of basic neuronal electrochemical
process. The Action potential (AP) from neuron has been recorded with microelectrodes at the
cellular level. Essentially the synaptic fibers, terminal boutons ,neuronal membrane and axon
contribute the distinguishable response characteristic .Electrical reaction of neurons includes the
following potential.
         g
       1. Presynnaptic spike potential (rapid ms positive event resulting from presynaptic
                                          (rapid-ms
          depolarization)
       2. Excitatory postsynaptic potential (EPSP) (Prolonged 2ms graded positive potential)
       3. Spike potential (High voltage ,sudden 2 ms positive discharge of 10 to 30 mv.
              ke
       4. After hyperpolarisation (prolong positive potential)
       5. Inhibitory post synaptic potential(IPSP)

10-20 EEG Electrode Placement system
   20
The amplitude, phase, and frequency of EEG signals depend on electrode placement. This
                       ,                            signals
placement is based on the frontal, parietal, temporal, and occipital cranial area. One of the most
popular schemes is the 10-20 EEG electrode placement system established by the international
                              20
Federation of EEG Societies usually employed to record spontaneous EEG. The Head is mapped
                                                                          EEG.
by four standard points: the nasion , the inions, and the left and right ears. Here 21 electrodes are
located on the surface of scalp. The position are determined as follows: Reference points are
                                  .                                                        poi
nasion, which is the delve at the top of the nose, level with the eyes, and inions, which is bony
lump at the base of the skull on the midline at the bback of the head. From these points, the skull
perimeters are measured in the transverse and media planes. Electrode are placed by measuring
                                                 median
the nasion-inions distance are marking points on the head 10%, 20%, 20%, 20%, 20%, 10% of
this length. The vertex, c2 electrode is the mid
                                             mid-point as shown in fig below.




Figure 3:10-20 EEG Electrode Placement system
            20




                                                180
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME

Signal Acquisition
Electrode arrangements may be either unipolar or bipolar. A unipolar arrangements is composed
of a number of scalp leads connected to a common indifference point such as earlobe. Hence
one electrode is common to all channels. A bipolar arrangements is achieved by the
interconnection of scalp electrodes.
ACTUAL CIRCUIT DIAGRAMS
The differential amplifier (as shown in Figure 4) is used where a difference in potential has to be
amplified in the presence of an interfering common-mode voltage. Such interference frequently
comes from 50 Hz power line fields, man-made electrical noise, or radiation from other electrical
equipment.




                Figure 4 shows the basic circuit arrangement for a differential amplifier.
If an out-of-phase (differential) voltage is applied to the inputs of amplifiers A and B, the current
flow in A will increase while the current in B will decrease. If both amplifiers and associated
parts are identical, the amplifier currents i1 and i2 will be equal and opposite, the net current will
be zero and no voltage drop will occur across resistor RC. Under these conditions the output
signal is a function of twice the gain of one amplifier.
If both signal inputs are in phase, both (+) or (–), this is called the common-mode signal. In the
case of such signals from the pickup power lines, both i1 and i2 will increase or decrease
simultaneously, causing a voltage drop on RC. This common-mode voltage results in
degeneration (voltage drop on RC) and a reduction in amplifier gain.
Of key importance is the ratio of the [differential signal gain (Adiff)] to the [common-mode gain
(Acm)]. This relationship is called the common-mode rejection ratio (CMRR) and is expressed by
equation (2-1)
CMRR = 20LOG10 Adiff /Acm          (2-1)
Where the CMRR is expressed in decibels.
The CMRR shows the ability of a differential amplifier to attenuate common-mode signals
appearing simultaneously with differential signals. CMRR is a ratio of two gains, as shows in
equation (2-2)
CMRR = 20 Log10 (Voutdiff/Vindiff)/(VOut/Vincm)                                 (2-2)
In determining the CMRR, the two signals (common-mode and differential) are adjusted at the
input to produce the same output voltage. For equation (2-2), Voutcm–Voutdiff.
Therefore, CMRR can be computed from the input voltages as shows in equation (2-3).

   CMRR = 20 Log10 Vindiff/Vincm    (2-3).

                                                 181
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME

 The CMRR is then +65 dB. Good amplifiers have CMRR values which range from +60 dB to
+100 dB. When two or more such amplifiers are cascaded, the CMRR of each can be add.




                           Fig 5 One Quarter of a TL074 IC (LF347)
The transistors Q1-Q2 are the input differential pair, while transistors Q3, Q5 and Q6 make the
common emitter resistor (RE). CMRR is 80 to 86 dB, and the bandwidth (open loop) is 3 MHz.
The input impedance is high, about 1012 ohms and the output impedance is low under 100 ohms.
The high-input impedance of the amplifier is a necessary design element, since the amplifier
could easily load down the signal source.
The adverse effect of electrode contact resistance on signal input is an additional consideration.
Losses incurred at the lead contacts with the skin decrease the available input signal to each
amplifier. In the circuit shown in Figure 6, the electrode contact resistors (R3, R4) are in series.
High input impedance of the amplifier, however, minimizes the signal loss.
The input signal to an amplifier from an EEG voltage is dependent both on the amplifier’s input
impedance and on the resistance of the electrodes placed on the Patient’s body. The input voltage
depends on the losses at the lead contacts. This is shown by equation (2-4).
EEGin =Zin of amplifier x EEG voltage source/Zin of amplifier + R of electrodes (2-4)
It can be shown using equation (2-4) that only 5-10% of the EEG signal is lost. Under good
conditions, a loss of less than one percent is possible. Since the TL074 has an input of over 1012
ohms, only a very small portion of the input signal voltage would be lost.
Since the TL074 is a quad, three of the amplifiers can be used for recording the ECG, EMG, or
EEG signal voltage. Two amplifiers are used for the differential input, and one for a single-ended
output amplifier. A typical circuit arrangement is shown in Figure 5.The gain of the inverting
amplifier A4 is determined by the ratio of the R1/R8 (100KΩ/8.2KΩ) resistors (A = 12). The
gain of the differential amplifier is determined from equation (2-5).
                    Adiff = 2       R6/R5          (2-5)




                                                182
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME




                    Fig 6 Differential input, Single-ended output amplifier
The gain (shown in the circuit diagram of Figure 5 is :
               Adiff = 2 x 100 KΩ/ 6.8KΩ
                Adiff = 29
The gain of the input stage can be controlled by varying the 10 KΩ resistor (R5) between pins 6
and 13. As the resistor is made smaller, the gain is increased. This resistor should not be zero
since the circuit might oscillate under high gain conditions. In the circuit shown, resistors of 1%
tolerance (or less) should be used, since both halves of the circuit must match. The value of R10
should be adjusted to obtain the best balance of the two signals input to A4. This balance
achieves the highest possible CMRR.
PATIENT LEAD SAFETY
In medical instrumentation, patient safety is a major consideration. If patient is totally isolated
from ground, the potential shock hazard is greatly reduced. Figure 7 shows how, by using an
optocoupler, a differential instrumentation amplifier can be isolated from the circuits that follow,
as well as from the earth ground.The optocoupler contains an infrared LED and a sensor. The
sensor can be a photodiode, phototransistor, or photodarlington. The LED-to-sensor insulation
breakdown voltage ranges from 1500 volts to over 20,000 volts, depending on the device used.




Fig 7: Floating differential amplifier

                                                183
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME

Observation
Each electrode site was rubbed with alcohol, electrode paste and electrodes firmly attached. For
each pressing of amplitude variation switch, there will be waveform generated for earlobe,
central, parietal, frontal, occipital points. The high-frequency roll-off was controlled by leaving
switches A1 and A2 open or closed, and the low end was rolled off at approximately 3 Hz by
opening A4. For a one-volt amplifier output, a system gains of 5,000 to 50,000 (75-95 dB) was
required. Digital oscilloscope was used to record data from EEG recorder. The output was seen
from TP15 on the oscilloscope and also seen the waveforms through DSO on the PC by
connecting the output (TP-15) to Ch1/Ch2 through parallel port. The output (TP15) of the
experimental panel can be seen on the Digital storage oscilloscope (DSO). Keep the DIP
switches SWA and SWB in kit as follows:




The various Practical waveform at different points are as shown below:
1. PointsF3-C3        4. PointsP3-O1




2.Points O1-O2             5.Points A2-O2




3.Points F4-C4            6.Points C4- P4




                                                184
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME




The various ideal waveforms at different points are shown below:




CONCLUSION
The EEG gives a coarse view of neural activity and can be used to non-invasively study cognitive
processes and the physiology of the brain. A typical EEG signal ranges from 1 to 100 µv peak to peak at
low frequencies(1 to 50 Hz) at the surface of scalp. Modern EEG machines are PC Based. EEG signal is
usually recorded by reusable scalp disc or cup electrodes . The EEG is composed of electrical rhythms
and transient discharges which are distinguished by location, frequency, amplitude, form, periodicity and
functional properties. The low-level EEG Signal from the brain as recorded is amplified and converted to
a digital signal for further processing. EEG machines have a notch filter sharply tuned at 50Hz so as to
eliminate main frequency interference Reading has been taken in international 10-20 Electrode system
for a normal person. EEG signal contain a mixture of signals
    1) Evoked potential(EP) and event related potential(ERP)components associated with the task
       monitored.
    2) 50 Hz main interference and electrophysiological signals such as eye movements, blinks and
       muscle activity.
REFERENCES
[1] P. Loiseau, “Epilepsies,” in Guide to Clinical Neurology. New York:Churchill Livingstone, 1995, pp.
903–914.
[2] G. Dumermuth, “Possibilities of electronic EEG processing in epileptology,”in Epileptology: Proc.
7th Int. Symp. on Epilepsy, 1976. pp.365–372.
[3] J. M. Clark, “Oxygen toxicity,” in The Physiology and Medicine of Diving, 3rd ed. San Pedro, CA:
Best. 1978, pp. 200–238.



                                                  185
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME

[4] F. H. Lopes da Silva, J. P. Pijn, and D. N. Veli, “Signal processing of EEG: evidence for chaos or
noise. An application to seizure activity inepilepsy,” in Advances in Processing and Pattern Analysis of
Biological Signals. New York: Plenum, 1996. pp. 21–32.
[5] J. R. G. Carrie, “A hybrid computer system for detecting and quantifying spike and wave EEG
patterns,” Electroencephalogr. Clin. Neurophysiol., vol. 33. pp. 339–341, 1972.
[6] E. E. Gose, S. Werner, and R. C. Bickford, “Computerized EEG spike detection,” in Proc. San Diego
Biomed. Symp., vol. 13. pp. 193–198,1974.
[7] J. R. Smith, “Automatic analysis and detection of EEG spikes,” IEEETrans. Biomed. Eng., vol. BME-
21. pp. 1–7, 1974.
[8] A. S. Gevins, C. L. Yeager, S. L. Diamond, G. M. Zeitlin, J. P. Spire, and A. H. Gevins, “Sharp-
transient analysis and threshold linear coherence spectra of paroxysmal EEG’s,” in Quantitative Analytic
Studies in Epilepsy. New York: Raven, 1976, pp. 463–481.
[9] F. H. Lopes da Silva, W. ten Broeke, K.van Hulten, and J. G. Lommen, “EEGnonstationarities
detected by inverse filtering in scalp and cortical recordings of epileptics: Statistical analysis and spatial
display,” in Quantitative Analytic Studies in Epilepsy. New York: Raven, 1976,pp. 375–387.
[10] J. Gotman and P. Gloor, “Automatic recognition and quantification of interictal epileptic activity,”
Electroenceph. Clin. Neurophysiol., vol. 4. pp. 513–529, 1976.
[11] K. M. Ma, G. G. Celesia, and W. P. Birkemeier, “Cluster analysis and spike detection in EEG,” in
Epileptology: Proc. 7th Int. Symp. On Epilepsy, 1976, pp. 386–396.
[12] W. P. Birkemeier, A. B. Fontaine, G. G. Celesia, and K. M. Ma, “Pattern recognition techniques for
the detection of epileptic transients of EEG,” IEEE Trans. Biomed. Eng., vol. BME-25, pp. 213–217,
1978.
[13] J. D. Frost, Jr., “Automatic recognition and characterization of epileptiform discharges in the human
EEG,” J. Clin. Neurophysiol., vol. 2. pp. 231–249, 1985.
[14] A. Babloyantz and A. Destexhe. “Low dimensional chaos in an instance of epilepsy,” Proc. Nat.
Acad. Sci. USA, vol. 83, pp. 3513–3517, 1986.
[15] P. E. Rapp, “Oscillations and chaos in cellular metabolism and physiological systems,” in Chaos-
Nonlinear Science: Theory and Applications.Manchester, U.K.: Manchester Univ. Press, 1986. pp. 179–
208.
[16] J. Gotman and L. Y. Wang, “State-dependent spike detection: Concepts and preliminary results,”
Electroencephalogr. Clin. Neurophysiol., vol. 79, pp. 11–19, 1991.
[17] A. P. Pjin, J. Van Neerven, N. Noestt, and F. H. Lopes Da Silva, “Chaos or noise in EEG signals:
Dependence on state and brain site,” Electroencephalorgr. Clin. Neurophysiol., vol. 79. pp. 371–381,
1991.
[18] A.C. Metting VanRijn, A. Peper, C.A. Grimbergen. Instrumentation Amplifier for bioelectric events:
a design with minimal number of parts. August, 21, 2008 Academic Medical Center, Medical
Physics Department
[19]“The Isolation Mode Rejection Ratio in Bioelectric Amplifiers.” A. C. MettingVanRijn, A. Peper,C.
A. Grimbergen. Academic Medical Center, Medical Physics Department
[20]“High Quality Recording of Bioelectric Events. II : A Low-Noise, Low-Power Multi channel
Amplifier Design.A. C. MettingVanRijn, A. Peper, C. A. Grimbergen. Academic Medical Center,
Medical Physics Department.
[21] Ranggayan, Rangaraj M. (2002). Biomedical Signal Analysis. IEEE Press. John Wiley & Sons, Inc.
pp. 28-31
[22] Reddy DC. (2005). Biomedical Signal Processing: Principles and Techniques. Mc Graw Hill pp. 128-135
[23]Northrop, Robert B. (2001). Noninvasive Instrumentation and Measurement in Medical Diagnosis. CRC Pr I
Llc. pp. 92-128
[24]Neuman, Michael R. (1998). Medical Instrumentation Application and Design. John Wiley & Sons, Inc pp.183-
233


                                                     186

More Related Content

What's hot

What's hot (20)

EEG Variants with patterns by Murtaza Syed
EEG Variants with patterns by Murtaza SyedEEG Variants with patterns by Murtaza Syed
EEG Variants with patterns by Murtaza Syed
 
EEG Artifact and How to Resolve
EEG Artifact and How to ResolveEEG Artifact and How to Resolve
EEG Artifact and How to Resolve
 
EEG basic to practice 1
EEG basic to practice 1EEG basic to practice 1
EEG basic to practice 1
 
Eeg machine filters
Eeg machine  filtersEeg machine  filters
Eeg machine filters
 
Artifact removal from ambulatory eeg
Artifact removal from ambulatory eegArtifact removal from ambulatory eeg
Artifact removal from ambulatory eeg
 
normal eeg
 normal eeg  normal eeg
normal eeg
 
Eeg basics.drjma
Eeg basics.drjmaEeg basics.drjma
Eeg basics.drjma
 
Principles of polarity in eeg
Principles of polarity in eegPrinciples of polarity in eeg
Principles of polarity in eeg
 
EEG artifacts
EEG artifactsEEG artifacts
EEG artifacts
 
Abnormal EEG patterns
Abnormal EEG patternsAbnormal EEG patterns
Abnormal EEG patterns
 
Event Related Potentials
Event Related PotentialsEvent Related Potentials
Event Related Potentials
 
Neonatal EEG Patterns
Neonatal EEG PatternsNeonatal EEG Patterns
Neonatal EEG Patterns
 
EEG artifacts
EEG  artifactsEEG  artifacts
EEG artifacts
 
EEG INTERPRETATION
EEG INTERPRETATIONEEG INTERPRETATION
EEG INTERPRETATION
 
Introduction to EEG: Instrument and Acquisition
Introduction to EEG: Instrument and AcquisitionIntroduction to EEG: Instrument and Acquisition
Introduction to EEG: Instrument and Acquisition
 
Electroencephalography (EEG) - Basics
Electroencephalography (EEG) - BasicsElectroencephalography (EEG) - Basics
Electroencephalography (EEG) - Basics
 
EEG - Montages, Equipment and Basic Physics
EEG - Montages, Equipment and Basic PhysicsEEG - Montages, Equipment and Basic Physics
EEG - Montages, Equipment and Basic Physics
 
Normal Neonatal EEG
Normal Neonatal EEGNormal Neonatal EEG
Normal Neonatal EEG
 
Impedence in EEG ( electroencephalography )
Impedence in EEG  ( electroencephalography ) Impedence in EEG  ( electroencephalography )
Impedence in EEG ( electroencephalography )
 
Eeg examples
Eeg examplesEeg examples
Eeg examples
 

Viewers also liked

Viewers also liked (7)

EEG
EEGEEG
EEG
 
EEG: Basics
EEG: BasicsEEG: Basics
EEG: Basics
 
Electroencephalography
ElectroencephalographyElectroencephalography
Electroencephalography
 
Classification of EEG Signals for Brain-Computer Interface
Classification of EEG Signals for Brain-Computer InterfaceClassification of EEG Signals for Brain-Computer Interface
Classification of EEG Signals for Brain-Computer Interface
 
Electroencephalogram(EEG)
Electroencephalogram(EEG)Electroencephalogram(EEG)
Electroencephalogram(EEG)
 
Normal vs abnormal
Normal vs abnormalNormal vs abnormal
Normal vs abnormal
 
brain computer-interfaces PPT
 brain computer-interfaces PPT brain computer-interfaces PPT
brain computer-interfaces PPT
 

Similar to A review of eeg recording techniques

International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)ijceronline
 
Review:Wavelet transform based electroencephalogram methods
Review:Wavelet transform based electroencephalogram methodsReview:Wavelet transform based electroencephalogram methods
Review:Wavelet transform based electroencephalogram methodsijtsrd
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)ijceronline
 
Clinical teaching on electroencephelography
Clinical teaching on electroencephelographyClinical teaching on electroencephelography
Clinical teaching on electroencephelographyAquiflal KM
 
Chapter (1) Introduction to EEG
Chapter (1) Introduction to EEGChapter (1) Introduction to EEG
Chapter (1) Introduction to EEGkamal2011
 
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel Chair
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairModelling and Analysis of EEG Signals Based on Real Time Control for Wheel Chair
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
 
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
 
Denoising of EEG Signals for Analysis of Brain Disorders: A Review
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewDenoising of EEG Signals for Analysis of Brain Disorders: A Review
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewIRJET Journal
 
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
 
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparison
Analysis of EEG data Using ICA and Algorithm Development for Energy ComparisonAnalysis of EEG data Using ICA and Algorithm Development for Energy Comparison
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
 
NEUROPHYSIOLOGY technique.pptx
NEUROPHYSIOLOGY technique.pptxNEUROPHYSIOLOGY technique.pptx
NEUROPHYSIOLOGY technique.pptxMr SACHIN
 
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...sipij
 
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIER
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIEREEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIER
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
 

Similar to A review of eeg recording techniques (20)

Basics of EEG
Basics of EEGBasics of EEG
Basics of EEG
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
 
Review:Wavelet transform based electroencephalogram methods
Review:Wavelet transform based electroencephalogram methodsReview:Wavelet transform based electroencephalogram methods
Review:Wavelet transform based electroencephalogram methods
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
 
EEG_circut.ppt
EEG_circut.pptEEG_circut.ppt
EEG_circut.ppt
 
EEG_circuit.ppt
EEG_circuit.pptEEG_circuit.ppt
EEG_circuit.ppt
 
Clinical teaching on electroencephelography
Clinical teaching on electroencephelographyClinical teaching on electroencephelography
Clinical teaching on electroencephelography
 
Chapter (1) Introduction to EEG
Chapter (1) Introduction to EEGChapter (1) Introduction to EEG
Chapter (1) Introduction to EEG
 
A0510107
A0510107A0510107
A0510107
 
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel Chair
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairModelling and Analysis of EEG Signals Based on Real Time Control for Wheel Chair
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel Chair
 
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...
 
Denoising of EEG Signals for Analysis of Brain Disorders: A Review
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewDenoising of EEG Signals for Analysis of Brain Disorders: A Review
Denoising of EEG Signals for Analysis of Brain Disorders: A Review
 
Cr31618621
Cr31618621Cr31618621
Cr31618621
 
Eeg
EegEeg
Eeg
 
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...
 
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparison
Analysis of EEG data Using ICA and Algorithm Development for Energy ComparisonAnalysis of EEG data Using ICA and Algorithm Development for Energy Comparison
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparison
 
NEUROPHYSIOLOGY technique.pptx
NEUROPHYSIOLOGY technique.pptxNEUROPHYSIOLOGY technique.pptx
NEUROPHYSIOLOGY technique.pptx
 
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...
 
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIER
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIEREEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIER
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIER
 
Ab044195198
Ab044195198Ab044195198
Ab044195198
 

More from iaemedu

Tech transfer making it as a risk free approach in pharmaceutical and biotech in
Tech transfer making it as a risk free approach in pharmaceutical and biotech inTech transfer making it as a risk free approach in pharmaceutical and biotech in
Tech transfer making it as a risk free approach in pharmaceutical and biotech iniaemedu
 
Integration of feature sets with machine learning techniques
Integration of feature sets with machine learning techniquesIntegration of feature sets with machine learning techniques
Integration of feature sets with machine learning techniquesiaemedu
 
Effective broadcasting in mobile ad hoc networks using grid
Effective broadcasting in mobile ad hoc networks using gridEffective broadcasting in mobile ad hoc networks using grid
Effective broadcasting in mobile ad hoc networks using gridiaemedu
 
Effect of scenario environment on the performance of mane ts routing
Effect of scenario environment on the performance of mane ts routingEffect of scenario environment on the performance of mane ts routing
Effect of scenario environment on the performance of mane ts routingiaemedu
 
Adaptive job scheduling with load balancing for workflow application
Adaptive job scheduling with load balancing for workflow applicationAdaptive job scheduling with load balancing for workflow application
Adaptive job scheduling with load balancing for workflow applicationiaemedu
 
Survey on transaction reordering
Survey on transaction reorderingSurvey on transaction reordering
Survey on transaction reorderingiaemedu
 
Semantic web services and its challenges
Semantic web services and its challengesSemantic web services and its challenges
Semantic web services and its challengesiaemedu
 
Website based patent information searching mechanism
Website based patent information searching mechanismWebsite based patent information searching mechanism
Website based patent information searching mechanismiaemedu
 
Revisiting the experiment on detecting of replay and message modification
Revisiting the experiment on detecting of replay and message modificationRevisiting the experiment on detecting of replay and message modification
Revisiting the experiment on detecting of replay and message modificationiaemedu
 
Prediction of customer behavior using cma
Prediction of customer behavior using cmaPrediction of customer behavior using cma
Prediction of customer behavior using cmaiaemedu
 
Performance analysis of manet routing protocol in presence
Performance analysis of manet routing protocol in presencePerformance analysis of manet routing protocol in presence
Performance analysis of manet routing protocol in presenceiaemedu
 
Performance measurement of different requirements engineering
Performance measurement of different requirements engineeringPerformance measurement of different requirements engineering
Performance measurement of different requirements engineeringiaemedu
 
Mobile safety systems for automobiles
Mobile safety systems for automobilesMobile safety systems for automobiles
Mobile safety systems for automobilesiaemedu
 
Efficient text compression using special character replacement
Efficient text compression using special character replacementEfficient text compression using special character replacement
Efficient text compression using special character replacementiaemedu
 
Agile programming a new approach
Agile programming a new approachAgile programming a new approach
Agile programming a new approachiaemedu
 
Adaptive load balancing techniques in global scale grid environment
Adaptive load balancing techniques in global scale grid environmentAdaptive load balancing techniques in global scale grid environment
Adaptive load balancing techniques in global scale grid environmentiaemedu
 
A survey on the performance of job scheduling in workflow application
A survey on the performance of job scheduling in workflow applicationA survey on the performance of job scheduling in workflow application
A survey on the performance of job scheduling in workflow applicationiaemedu
 
A survey of mitigating routing misbehavior in mobile ad hoc networks
A survey of mitigating routing misbehavior in mobile ad hoc networksA survey of mitigating routing misbehavior in mobile ad hoc networks
A survey of mitigating routing misbehavior in mobile ad hoc networksiaemedu
 
A novel approach for satellite imagery storage by classify
A novel approach for satellite imagery storage by classifyA novel approach for satellite imagery storage by classify
A novel approach for satellite imagery storage by classifyiaemedu
 
A self recovery approach using halftone images for medical imagery
A self recovery approach using halftone images for medical imageryA self recovery approach using halftone images for medical imagery
A self recovery approach using halftone images for medical imageryiaemedu
 

More from iaemedu (20)

Tech transfer making it as a risk free approach in pharmaceutical and biotech in
Tech transfer making it as a risk free approach in pharmaceutical and biotech inTech transfer making it as a risk free approach in pharmaceutical and biotech in
Tech transfer making it as a risk free approach in pharmaceutical and biotech in
 
Integration of feature sets with machine learning techniques
Integration of feature sets with machine learning techniquesIntegration of feature sets with machine learning techniques
Integration of feature sets with machine learning techniques
 
Effective broadcasting in mobile ad hoc networks using grid
Effective broadcasting in mobile ad hoc networks using gridEffective broadcasting in mobile ad hoc networks using grid
Effective broadcasting in mobile ad hoc networks using grid
 
Effect of scenario environment on the performance of mane ts routing
Effect of scenario environment on the performance of mane ts routingEffect of scenario environment on the performance of mane ts routing
Effect of scenario environment on the performance of mane ts routing
 
Adaptive job scheduling with load balancing for workflow application
Adaptive job scheduling with load balancing for workflow applicationAdaptive job scheduling with load balancing for workflow application
Adaptive job scheduling with load balancing for workflow application
 
Survey on transaction reordering
Survey on transaction reorderingSurvey on transaction reordering
Survey on transaction reordering
 
Semantic web services and its challenges
Semantic web services and its challengesSemantic web services and its challenges
Semantic web services and its challenges
 
Website based patent information searching mechanism
Website based patent information searching mechanismWebsite based patent information searching mechanism
Website based patent information searching mechanism
 
Revisiting the experiment on detecting of replay and message modification
Revisiting the experiment on detecting of replay and message modificationRevisiting the experiment on detecting of replay and message modification
Revisiting the experiment on detecting of replay and message modification
 
Prediction of customer behavior using cma
Prediction of customer behavior using cmaPrediction of customer behavior using cma
Prediction of customer behavior using cma
 
Performance analysis of manet routing protocol in presence
Performance analysis of manet routing protocol in presencePerformance analysis of manet routing protocol in presence
Performance analysis of manet routing protocol in presence
 
Performance measurement of different requirements engineering
Performance measurement of different requirements engineeringPerformance measurement of different requirements engineering
Performance measurement of different requirements engineering
 
Mobile safety systems for automobiles
Mobile safety systems for automobilesMobile safety systems for automobiles
Mobile safety systems for automobiles
 
Efficient text compression using special character replacement
Efficient text compression using special character replacementEfficient text compression using special character replacement
Efficient text compression using special character replacement
 
Agile programming a new approach
Agile programming a new approachAgile programming a new approach
Agile programming a new approach
 
Adaptive load balancing techniques in global scale grid environment
Adaptive load balancing techniques in global scale grid environmentAdaptive load balancing techniques in global scale grid environment
Adaptive load balancing techniques in global scale grid environment
 
A survey on the performance of job scheduling in workflow application
A survey on the performance of job scheduling in workflow applicationA survey on the performance of job scheduling in workflow application
A survey on the performance of job scheduling in workflow application
 
A survey of mitigating routing misbehavior in mobile ad hoc networks
A survey of mitigating routing misbehavior in mobile ad hoc networksA survey of mitigating routing misbehavior in mobile ad hoc networks
A survey of mitigating routing misbehavior in mobile ad hoc networks
 
A novel approach for satellite imagery storage by classify
A novel approach for satellite imagery storage by classifyA novel approach for satellite imagery storage by classify
A novel approach for satellite imagery storage by classify
 
A self recovery approach using halftone images for medical imagery
A self recovery approach using halftone images for medical imageryA self recovery approach using halftone images for medical imagery
A self recovery approach using halftone images for medical imagery
 

A review of eeg recording techniques

  • 1. International INTERNATIONALCommunication Engineering & Technology (IJECET), ISSN 0976 – Journal of Electronics and JOURNAL OF ELECTRONICS AND 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), pp. 177-186 IJECET © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2012): 3.5930 (Calculated by GISI) ©IAEME www.jifactor.com A REVIEW OF EEG RECORDING TECHNIQUES Imteyaz Ahmad1, F Ansari2, U.K. Dey3 1 Dept of ECE , 2Dept of Electrical Engg. , 3Dept of Mining Engg. BIT Sindri, Dhanbad, Jharkhand 1 Corresponding Author Tel.-+91-0326-2245671 Fax no.-+91-0326-2350729 Email address for correspondence author-imtiazahmadbitsindri@gmail.com ABSTRACT EEG is a collaborative tool to diagnosis brain function and disease. Modern interpretation of EEG origin rest with knowledge of basic neuronal electrochemical process. The EEG is composed of electrical rhythms and transient discharges which are distinguished by location, frequency, amplitude, form, periodicity and functional properties. The low-level EEG Signal from the brain as recorded is amplified and converted to a digital signal for further processing. EEG machines have a notch filter sharply tuned at 50Hz so as to eliminate main frequency interference. Reading has been taken in international 10-20 Electrode system for a normal person. EEG signal voltage amplitudes ranges 1 to 100 µv peak to peak at low frequencies(1 to 50 Hz) at the surface of scalp. In biofeedback training the ratio of amplitude of alpha to theta waves need to be increased. Keyword: Electroencephalogram(EEG), biofeedback, notch filter. INTRODUCTION The human brain contains approximately 100 billion nerve cells called neurons. Neurons have the amazing ability to gather and transmit electrochemical signals. The Neurons have 3 basic parts, a cell body which has the necessary cells components, Axon which is like a long cable to carry nerve impulse and finally the Dendrites which is the nerve ending branches that connects to other cells to allow electrical transfers between cells. The generation of EEG potentials requires a neural source close to the inside surface of the skull that is coherent, which means all the neurons must be aligned similarly and act together electrically. Pyramidal cells in the center of the cerebral cortex are the major source of EEG potentials. The dendrites will receive excitatory or inhibitory inputs from surrounding neurons and axons. When the dendrites receive an impulse or input by an ion such as Na+ enters them (become active), current flows into and out of these dendritic processes and the cell body. The cell to dendrite relationship is therefore one of a 177
  • 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME constantly shifting current dipole, and variations in orientation and strength of the dipole produce wave like fluctuations in a volume conductor. When the sum of electrical activity is negative relative to cell, the cell is depolarized and quite excitable. When it is positive, the cell is hyperpolarized and less excitable. EEG potentials on the scalp are usually no more than 150uV peak to peak. The brain frequencies depended on the degree of activity of the cerebral cortex. For example, the waves change between states of wakefulness and sleep. Much of the time, the brain waves are irregular and no general pattern can be observed. Yet at other times, distinct patterns do occur. Some of these are characterized to be abnormalities of the brain such as epilepsy. Generally there are four wave groups (alpha, beta, theta, and delta). The EEG rhythm and waveforms are varied by the position of electrode placements on certain parts of the brain (fig.1). Alpha wave occurs at a frequency between 7.5 and 13Hz. The alpha waves are produced when a person is in a conscious, relaxed state with eyes closed; the activity is suppressed when the eyes are open. The amplitude of the alpha rhythm is largest and intensely occurs in the occipital region and can be best recorded at parietal and frontal regions of the scalp. Beta waves normally occur in the frequency range of 14-30Hz and sometimes even as high as 50Hz for intense activity. Beta waves activities are present when people are alert or anxious, with their eyes open. Theta potentials are large amplitude, low frequency between 3.5 and 7.5Hz waves. Theta is abnormal in alert adults but seen during sleep, and small children. Theta waves occur mainly in the parietal and temporal region. Delta waves have the largest amplitudes and the lowest frequency in less than 3.5Hz. It is normal rhythm for infants less than one year old and in adults in deep sleep. This wave can thus occur solely within the cortex, independent of the activities in lower regions of the brain. Electroencephalography (EEG) is the measurement of electrical activity produced by the brain as recorded from electrodes placed on the scalp. Just as the activity in a computer can be understood on multiple levels from the activity of individual transistors to the function of applications, so can the electrical activity of the brain be described on the relatively small to relatively large scales. At one end are action potentials in a single axon or currents with a single dendrite of a single neurons at the other end is the activity measured by the EEG which aggregates the electric voltage field from million of neurons .so called scalp EEG is collected from tens to hundreds of electrodes positioned on different location at the surface of the head EEG signal ( in the range of milli volts) are amplified and digitalized for later processing. The data measured by the scalp EEG are used for clinical and research purposes. In neurology the main diagnostic application of EEG is for epilepsy by the this technique is also used to investigate many other pathologies such as sleep related disordered ,sensory deficits, brain tumors, etc. In cognitive neuroscience, EEG is used to investigate the neural correlates of mental 178
  • 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME activity from low level perceptual and motor processes to higher order cognition (attention, memory, reading etc.) EEG electrodes transfer ionic currents from cerebral tissue into electrical currents used in EEG preamplifiers. The electrical characteristic are determined primarily by the type of metal used. silver-silver chloride (Ag-Ag cl) is commonly found in electrode discs. Five types of electrode are used: 1.Sclap- silver pads, discs, or cup; stainless steel rods; and chlorides silver wires. 2. Sphenoidal- alternating insulated silver and bare wire and chloride tip inserted through muscle tissue by a needle. 3. Nasopharyngeal- silver rod with ball at the tip inserted through the nostrils. 4.Eltrocorticographic- cotton wicks soaked in saline solution that rest on the brain surface. 5. Intercerebral- sheaves of Teflon-coated gold or platinum wire cut a various distance from the sheaf tip and used to electrically stimulate the brain. BASIC RECORDING SYSTEM Referential: The potential difference is measured between an active electrode and an inactive reference electrode Figure 2 (a) Referential method Bipolar: The potential difference is measured between two active electrode Figure 2(b) Bipolar method EEG is a representation of the electrical activity of the brain. .The technique involve following: 1.Biopotential peak up- Cranial or cerebral surface transducer electrodes 2.EEG signal conditioning-Transducer output amplification and filtering (.1 to 100Hz) 3.EEG signal recording-Signal displayed on graphic recorder or CRT 4. EEG Signal analysis- visual or computer interpretation of resting EEG EEG is a collaborative tool is diagnosis brain function and disease. Many Physician and neurologist view EEG signal as interest artifacts but confess that then are not certain of the signal 179
  • 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October December (2012), © IAEME October- origins. In fact ,until recently EEG waveform were originally thought to be a summation of action potential of neurons as they made their was to cranial surface later idea reflects stimulation associated by diverse neuron . Modern interpretation of EEG origin rest with knowledge of basic neuronal electrochemical process. The Action potential (AP) from neuron has been recorded with microelectrodes at the cellular level. Essentially the synaptic fibers, terminal boutons ,neuronal membrane and axon contribute the distinguishable response characteristic .Electrical reaction of neurons includes the following potential. g 1. Presynnaptic spike potential (rapid ms positive event resulting from presynaptic (rapid-ms depolarization) 2. Excitatory postsynaptic potential (EPSP) (Prolonged 2ms graded positive potential) 3. Spike potential (High voltage ,sudden 2 ms positive discharge of 10 to 30 mv. ke 4. After hyperpolarisation (prolong positive potential) 5. Inhibitory post synaptic potential(IPSP) 10-20 EEG Electrode Placement system 20 The amplitude, phase, and frequency of EEG signals depend on electrode placement. This , signals placement is based on the frontal, parietal, temporal, and occipital cranial area. One of the most popular schemes is the 10-20 EEG electrode placement system established by the international 20 Federation of EEG Societies usually employed to record spontaneous EEG. The Head is mapped EEG. by four standard points: the nasion , the inions, and the left and right ears. Here 21 electrodes are located on the surface of scalp. The position are determined as follows: Reference points are . poi nasion, which is the delve at the top of the nose, level with the eyes, and inions, which is bony lump at the base of the skull on the midline at the bback of the head. From these points, the skull perimeters are measured in the transverse and media planes. Electrode are placed by measuring median the nasion-inions distance are marking points on the head 10%, 20%, 20%, 20%, 20%, 10% of this length. The vertex, c2 electrode is the mid mid-point as shown in fig below. Figure 3:10-20 EEG Electrode Placement system 20 180
  • 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME Signal Acquisition Electrode arrangements may be either unipolar or bipolar. A unipolar arrangements is composed of a number of scalp leads connected to a common indifference point such as earlobe. Hence one electrode is common to all channels. A bipolar arrangements is achieved by the interconnection of scalp electrodes. ACTUAL CIRCUIT DIAGRAMS The differential amplifier (as shown in Figure 4) is used where a difference in potential has to be amplified in the presence of an interfering common-mode voltage. Such interference frequently comes from 50 Hz power line fields, man-made electrical noise, or radiation from other electrical equipment. Figure 4 shows the basic circuit arrangement for a differential amplifier. If an out-of-phase (differential) voltage is applied to the inputs of amplifiers A and B, the current flow in A will increase while the current in B will decrease. If both amplifiers and associated parts are identical, the amplifier currents i1 and i2 will be equal and opposite, the net current will be zero and no voltage drop will occur across resistor RC. Under these conditions the output signal is a function of twice the gain of one amplifier. If both signal inputs are in phase, both (+) or (–), this is called the common-mode signal. In the case of such signals from the pickup power lines, both i1 and i2 will increase or decrease simultaneously, causing a voltage drop on RC. This common-mode voltage results in degeneration (voltage drop on RC) and a reduction in amplifier gain. Of key importance is the ratio of the [differential signal gain (Adiff)] to the [common-mode gain (Acm)]. This relationship is called the common-mode rejection ratio (CMRR) and is expressed by equation (2-1) CMRR = 20LOG10 Adiff /Acm (2-1) Where the CMRR is expressed in decibels. The CMRR shows the ability of a differential amplifier to attenuate common-mode signals appearing simultaneously with differential signals. CMRR is a ratio of two gains, as shows in equation (2-2) CMRR = 20 Log10 (Voutdiff/Vindiff)/(VOut/Vincm) (2-2) In determining the CMRR, the two signals (common-mode and differential) are adjusted at the input to produce the same output voltage. For equation (2-2), Voutcm–Voutdiff. Therefore, CMRR can be computed from the input voltages as shows in equation (2-3). CMRR = 20 Log10 Vindiff/Vincm (2-3). 181
  • 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME The CMRR is then +65 dB. Good amplifiers have CMRR values which range from +60 dB to +100 dB. When two or more such amplifiers are cascaded, the CMRR of each can be add. Fig 5 One Quarter of a TL074 IC (LF347) The transistors Q1-Q2 are the input differential pair, while transistors Q3, Q5 and Q6 make the common emitter resistor (RE). CMRR is 80 to 86 dB, and the bandwidth (open loop) is 3 MHz. The input impedance is high, about 1012 ohms and the output impedance is low under 100 ohms. The high-input impedance of the amplifier is a necessary design element, since the amplifier could easily load down the signal source. The adverse effect of electrode contact resistance on signal input is an additional consideration. Losses incurred at the lead contacts with the skin decrease the available input signal to each amplifier. In the circuit shown in Figure 6, the electrode contact resistors (R3, R4) are in series. High input impedance of the amplifier, however, minimizes the signal loss. The input signal to an amplifier from an EEG voltage is dependent both on the amplifier’s input impedance and on the resistance of the electrodes placed on the Patient’s body. The input voltage depends on the losses at the lead contacts. This is shown by equation (2-4). EEGin =Zin of amplifier x EEG voltage source/Zin of amplifier + R of electrodes (2-4) It can be shown using equation (2-4) that only 5-10% of the EEG signal is lost. Under good conditions, a loss of less than one percent is possible. Since the TL074 has an input of over 1012 ohms, only a very small portion of the input signal voltage would be lost. Since the TL074 is a quad, three of the amplifiers can be used for recording the ECG, EMG, or EEG signal voltage. Two amplifiers are used for the differential input, and one for a single-ended output amplifier. A typical circuit arrangement is shown in Figure 5.The gain of the inverting amplifier A4 is determined by the ratio of the R1/R8 (100KΩ/8.2KΩ) resistors (A = 12). The gain of the differential amplifier is determined from equation (2-5). Adiff = 2 R6/R5 (2-5) 182
  • 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME Fig 6 Differential input, Single-ended output amplifier The gain (shown in the circuit diagram of Figure 5 is : Adiff = 2 x 100 KΩ/ 6.8KΩ Adiff = 29 The gain of the input stage can be controlled by varying the 10 KΩ resistor (R5) between pins 6 and 13. As the resistor is made smaller, the gain is increased. This resistor should not be zero since the circuit might oscillate under high gain conditions. In the circuit shown, resistors of 1% tolerance (or less) should be used, since both halves of the circuit must match. The value of R10 should be adjusted to obtain the best balance of the two signals input to A4. This balance achieves the highest possible CMRR. PATIENT LEAD SAFETY In medical instrumentation, patient safety is a major consideration. If patient is totally isolated from ground, the potential shock hazard is greatly reduced. Figure 7 shows how, by using an optocoupler, a differential instrumentation amplifier can be isolated from the circuits that follow, as well as from the earth ground.The optocoupler contains an infrared LED and a sensor. The sensor can be a photodiode, phototransistor, or photodarlington. The LED-to-sensor insulation breakdown voltage ranges from 1500 volts to over 20,000 volts, depending on the device used. Fig 7: Floating differential amplifier 183
  • 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME Observation Each electrode site was rubbed with alcohol, electrode paste and electrodes firmly attached. For each pressing of amplitude variation switch, there will be waveform generated for earlobe, central, parietal, frontal, occipital points. The high-frequency roll-off was controlled by leaving switches A1 and A2 open or closed, and the low end was rolled off at approximately 3 Hz by opening A4. For a one-volt amplifier output, a system gains of 5,000 to 50,000 (75-95 dB) was required. Digital oscilloscope was used to record data from EEG recorder. The output was seen from TP15 on the oscilloscope and also seen the waveforms through DSO on the PC by connecting the output (TP-15) to Ch1/Ch2 through parallel port. The output (TP15) of the experimental panel can be seen on the Digital storage oscilloscope (DSO). Keep the DIP switches SWA and SWB in kit as follows: The various Practical waveform at different points are as shown below: 1. PointsF3-C3 4. PointsP3-O1 2.Points O1-O2 5.Points A2-O2 3.Points F4-C4 6.Points C4- P4 184
  • 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME The various ideal waveforms at different points are shown below: CONCLUSION The EEG gives a coarse view of neural activity and can be used to non-invasively study cognitive processes and the physiology of the brain. A typical EEG signal ranges from 1 to 100 µv peak to peak at low frequencies(1 to 50 Hz) at the surface of scalp. Modern EEG machines are PC Based. EEG signal is usually recorded by reusable scalp disc or cup electrodes . The EEG is composed of electrical rhythms and transient discharges which are distinguished by location, frequency, amplitude, form, periodicity and functional properties. The low-level EEG Signal from the brain as recorded is amplified and converted to a digital signal for further processing. EEG machines have a notch filter sharply tuned at 50Hz so as to eliminate main frequency interference Reading has been taken in international 10-20 Electrode system for a normal person. EEG signal contain a mixture of signals 1) Evoked potential(EP) and event related potential(ERP)components associated with the task monitored. 2) 50 Hz main interference and electrophysiological signals such as eye movements, blinks and muscle activity. REFERENCES [1] P. Loiseau, “Epilepsies,” in Guide to Clinical Neurology. New York:Churchill Livingstone, 1995, pp. 903–914. [2] G. Dumermuth, “Possibilities of electronic EEG processing in epileptology,”in Epileptology: Proc. 7th Int. Symp. on Epilepsy, 1976. pp.365–372. [3] J. M. Clark, “Oxygen toxicity,” in The Physiology and Medicine of Diving, 3rd ed. San Pedro, CA: Best. 1978, pp. 200–238. 185
  • 10. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME [4] F. H. Lopes da Silva, J. P. Pijn, and D. N. Veli, “Signal processing of EEG: evidence for chaos or noise. An application to seizure activity inepilepsy,” in Advances in Processing and Pattern Analysis of Biological Signals. New York: Plenum, 1996. pp. 21–32. [5] J. R. G. Carrie, “A hybrid computer system for detecting and quantifying spike and wave EEG patterns,” Electroencephalogr. Clin. Neurophysiol., vol. 33. pp. 339–341, 1972. [6] E. E. Gose, S. Werner, and R. C. Bickford, “Computerized EEG spike detection,” in Proc. San Diego Biomed. Symp., vol. 13. pp. 193–198,1974. [7] J. R. Smith, “Automatic analysis and detection of EEG spikes,” IEEETrans. Biomed. Eng., vol. BME- 21. pp. 1–7, 1974. [8] A. S. Gevins, C. L. Yeager, S. L. Diamond, G. M. Zeitlin, J. P. Spire, and A. H. Gevins, “Sharp- transient analysis and threshold linear coherence spectra of paroxysmal EEG’s,” in Quantitative Analytic Studies in Epilepsy. New York: Raven, 1976, pp. 463–481. [9] F. H. Lopes da Silva, W. ten Broeke, K.van Hulten, and J. G. Lommen, “EEGnonstationarities detected by inverse filtering in scalp and cortical recordings of epileptics: Statistical analysis and spatial display,” in Quantitative Analytic Studies in Epilepsy. New York: Raven, 1976,pp. 375–387. [10] J. Gotman and P. Gloor, “Automatic recognition and quantification of interictal epileptic activity,” Electroenceph. Clin. Neurophysiol., vol. 4. pp. 513–529, 1976. [11] K. M. Ma, G. G. Celesia, and W. P. Birkemeier, “Cluster analysis and spike detection in EEG,” in Epileptology: Proc. 7th Int. Symp. On Epilepsy, 1976, pp. 386–396. [12] W. P. Birkemeier, A. B. Fontaine, G. G. Celesia, and K. M. Ma, “Pattern recognition techniques for the detection of epileptic transients of EEG,” IEEE Trans. Biomed. Eng., vol. BME-25, pp. 213–217, 1978. [13] J. D. Frost, Jr., “Automatic recognition and characterization of epileptiform discharges in the human EEG,” J. Clin. Neurophysiol., vol. 2. pp. 231–249, 1985. [14] A. Babloyantz and A. Destexhe. “Low dimensional chaos in an instance of epilepsy,” Proc. Nat. Acad. Sci. USA, vol. 83, pp. 3513–3517, 1986. [15] P. E. Rapp, “Oscillations and chaos in cellular metabolism and physiological systems,” in Chaos- Nonlinear Science: Theory and Applications.Manchester, U.K.: Manchester Univ. Press, 1986. pp. 179– 208. [16] J. Gotman and L. Y. Wang, “State-dependent spike detection: Concepts and preliminary results,” Electroencephalogr. Clin. Neurophysiol., vol. 79, pp. 11–19, 1991. [17] A. P. Pjin, J. Van Neerven, N. Noestt, and F. H. Lopes Da Silva, “Chaos or noise in EEG signals: Dependence on state and brain site,” Electroencephalorgr. Clin. Neurophysiol., vol. 79. pp. 371–381, 1991. [18] A.C. Metting VanRijn, A. Peper, C.A. Grimbergen. Instrumentation Amplifier for bioelectric events: a design with minimal number of parts. August, 21, 2008 Academic Medical Center, Medical Physics Department [19]“The Isolation Mode Rejection Ratio in Bioelectric Amplifiers.” A. C. MettingVanRijn, A. Peper,C. A. Grimbergen. Academic Medical Center, Medical Physics Department [20]“High Quality Recording of Bioelectric Events. II : A Low-Noise, Low-Power Multi channel Amplifier Design.A. C. MettingVanRijn, A. Peper, C. A. Grimbergen. Academic Medical Center, Medical Physics Department. [21] Ranggayan, Rangaraj M. (2002). Biomedical Signal Analysis. IEEE Press. John Wiley & Sons, Inc. pp. 28-31 [22] Reddy DC. (2005). Biomedical Signal Processing: Principles and Techniques. Mc Graw Hill pp. 128-135 [23]Northrop, Robert B. (2001). Noninvasive Instrumentation and Measurement in Medical Diagnosis. CRC Pr I Llc. pp. 92-128 [24]Neuman, Michael R. (1998). Medical Instrumentation Application and Design. John Wiley & Sons, Inc pp.183- 233 186