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1. Samaneh Valipour et al Int. Journal of Engineering Research and Applications
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ISSN : 2248-9622, Vol. 4, Issue 1( Version 1), January 2014, pp.154-159
RESEARCH ARTICLE
OPEN ACCESS
Detection of an Alpha Rhythm of EEG Signal Based On
EEGLAB
Samaneh Valipour1, A.D. Shaligram2, G.R.Kulkarni3
1
Department of Electronic Science, Pune University, pune, 411 007, Maharashtra,India
Department of Electronic Science, Pune University, pune, 411 007, Maharashtra,India
3
Department of Physics, Pune University, pune,411 007,Maharashtra,India
2
Abstract
The EEG (Electroencephalogram) is the electrical activity of brain that can be detected and measured by putting
electrodes according to international 10-20 system on the scalp. There are four major frequency rhythms in
EEG. Alpha is one of the frequency bands. This rhythm is very important because of its application in seizure
suppression and for treatment of depression in biofeedback method. Hence detection of alpha band’s place and
patient state has importance. However EEG waves contain useful information of brain, but we cannot see these
information by observing in time domain directly. Hence we have to analyze these waveforms by signal
processing techniques. In this paper, EEGLAB is used for processing and taking power spectrum density (PSD),
which explains how the power of a signal is distributed with frequency. PSD of different subjects with open and
closed eyes is shown in this paper. EEG data are downloaded from free database physionet in MIT/BIH
database. It is observed that alpha rhythm in back of head and with closed eyes has dominant power in
periodgram.
Keywords: Alpha rhythm, Artifact, dominant power, EEG,EEGLAB, PSD
I.
Introduction
An EEG (Electroencephalogram) is the
electrical activity of brain that can be used for
monitoring of abnormalities related to brain. These
waves are measured by putting electrodes which are as
Small metal discs with thin wires on the scalp and
sometime over the brain. Then these electrodes send
signals to a computer for recording the results. Pattern
of Normal EEG is recognizable. Hence, doctors can
look for abnormal patterns that indicate seizures and
other problems. The most common reason for usage of
an EEG is diagnosis of seizure disorders.
EEGs can also help to identify causes of other
problems such as sleep disorders and changes in
behavior. EEGs are sometimes used to evaluate brain
activity after a severe head injury or before heart or
liver transplantation [1]. Electrical activity is highly
random in nature and may contain useful information
about the brain state. They are basically non-linear and
nonstationary in nature. However, it is very difficult to
get useful information from these signals directly in
the time domain just by observing them
[2].Hence,advanced signal processing techniques are
necessary for considering of brain states(Fig-1).
Amplitude of EEG is the voltage (in microvolt range)
from 10 µV to 100 µV on the scalp and but when
measurement is from surface of brain (in open
operation) is 10 mv to 20mv.There are four common
frequency bands in EEG signals as below Table 1.
Table 1: Frequency and amplitude of different bands
Name of band
Frequency
Amplitude
delta
Less than 4 HZ
20µv to 200µv
theta
alpha
5 HZ to 7 HZ
8 HZ to 12.5 HZ
Less than 45µv
14µv to 45 µv
beta
14 HZ to 30HZ
2µv to 20µv
As is shown in table1 amplitude of any
frequency has some range. Intensity or value of
amplitude is depended to some factor such as state of
patient as well as location of electrode on the scalp.
Alpha is one of the frequency bands. Amplitude of
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that is 14µv to 45 µv and its frequency is 8 to 12.5
Hz.
Historically, they were thought to represent
the activity of the visual cortex in an idle state. More
recent papers have argued that they inhibit areas of
the cortex not in use, or alternatively that they play an
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2. Samaneh Valipour et al Int. Journal of Engineering Research and Applications
www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 1( Version 1), January 2014, pp.154-159
active role in network coordination and
bands acquired from the scalp falls in the higher
communication [3]. Alpha wave biofeedback has
Theta and smaller alpha waves. They subjected
gained interest for having some successes in humans
Better signal processing algorithms can be
for seizure suppression and for treatment of
implemented using other spectral estimation methods
depression [4].There are some methods for analyzing
like parametric method and non-parametric methods
of EEG signals such as linear, non-linear, time
(Modified periodogram, Welch, Multitaper methods)
domain, frequency domain and time_frequency
and wavelet transforms [8].
domain.Many researchers have used PSD or spectral
analysis for analyzing of EEG signals. This method
II.
Power Spectrum Density(PSD)
determines power of different frequencies. EEGLAB
One of the most important applications in
is an interactive MATLAB toolbox for processing
digital signal processing (DSP) fields is the power
continuous and event-related EEG, MEG and other
spectral of signals which are periodic or random. A
electrophysiological data. EEGLAB provides an
spectrum is presentation of magnitude against
interactive graphic user interface (GUI) allowing
frequency of one parameter. The power spectral
users to flexibly and interactively process their highdensity (PSD), explain how the power or energy of a
density EEG and other dynamic brain data using
signal is distributed across frequency. This term
independent component analysis (ICA) and/or
related to Fourier transform. Hence PSD is frequency
time/frequency analysis (TFA), as well as standard
domain analysis.
averaging methods [5] which are freely available. O.
For definition of Power Spectral Density there are
A. Padierna Sosa et al 2011 focused on Development
several diffinition that each one releated to type of
of an EEG signal processing program based on
those Processes.
EEGLAB. They concluded that EEGLAB is a
program that provides an accessible solution to the
III.
EEG signal analysis
EEG signal processing problem, its free distribution
The EEG signals were downloaded from the
and great service variety allows inexperienced users
EEG motor Movement /imagery dataset [in European
to be able to acquire some knowledge experimenting
data format(EDF)][9].subjects performed different
with EEGLAB [6].
motor task while 64 channels EEG as international
MD. Shahedul amin et al 2010, considered
10-20 system were recorded using the BCI2000
spectral analysis of human sleep EEG signals. He
system. Each subject performed 14 experimental
concluded we can go for delta band to find whether a
runs. Two one-minute baseline runs (one with eyes
person in sleeping or not. More specifically, check
open and one with eyes closed which are our
the PSD at 1.8 ~ 2.0 Hz and at 2.7 ~ 2.9 Hz. If the
purpose), and three two-minutes runs of each of the
person is sleeping then there would be a sharp change
four tasks with motor movement. In this paper we
at these positions [7].S. Deivanayagi and her group
will discuss two tasks of subjects with open eyes and
worked on Spectral Analysis of EEG Signals during
closed eyes (without motor movement) in first 10
Hypnosis. They found the spectral analysis of EEG
second. EEGLAB software is used for taking PSD of
during hypnosis shows the frequency bands in theta
these samples. Properties of these subjects are as
and alpha range as we expected. From the analysis,
below Table 2.
they concluded that during hypnosis, the frequency
Table 2: Property of EEG signals
subject
state
Frequency
Number
Sampling
date
Starting time
Stopping time
of samples
of
time
samples
1
Eyes
160
1600
6 ms
12/08/200
16:15:00:000
16:15:09:994
s001r01
open
9
1
Eyes
160
1600
6 ms
12/08/200
16:15:00:000
16:15:09:994
S001r02
closed
9
2
Eyes
160
1600
6 ms
12/08/200
16:15:00:000
16:15:09:994
S002r01
open
9
2
Eyes
160
1600
6 ms
12/08/200
16:15:00:000
16:15:09:994
S002r02
closed
9
3
Eyes
160
1600
6 ms
12/08/200
16:15:00:000
16:15:09:994
S003r01
open
9
3
Eyes
160
1600
6 ms
12/08/200
16:15:00:000
16:15:09:994
S003r02
close
9
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3. Samaneh Valipour et al Int. Journal of Engineering Research and Applications
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ISSN : 2248-9622, Vol. 4, Issue 1( Version 1), January 2014, pp.154-159
IV.
Result and discussions:
For considering that in which part of head
and what states of patients alpha rhythm is dominant,
some experiment is done based EEGLAB. PSD of
some channels of the PZ, P3, P4, FZ and CZ for
frequency range 2Hz to 25Hz is taken out and are
shown in below figures. 3 subjects with open eyes
and closed eyes were considered in this work. As is
mentioned in table 1, frequency of alpha wave is
around 8 Hz to 12.5 Hz. Hence we focused on this
range.
I) Experiments of subject 1:
a) S001r02cz: eyes closed
b) s001r01cz: eyes open
Alpha band
Alpha band
Fig.1-1: PSD of CZ
a) S001r02Fz: eyes closed
b) s001r01fz: eyes open
Fig.1-2: PSD of FZ
a) S001r02Pz: eyes closed
b) s001r01pz: eyes open
Fig.1-3: PSD of PZ
a) S001r02P3: eyes closed
b) s001r01p3: eyes open
Fig.1-4: PSD of P3
a)S001r02P4:eyes closed
b)s001r01p4:eyes open
Fig1-5.: PSD of P4
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4. Samaneh Valipour et al Int. Journal of Engineering Research and Applications
www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 1( Version 1), January 2014, pp.154-159
Channel
Cz
Fz
Pz
P3
P4
Table 3: Output of experiment for subject 1
Maximum of amplitude in alpha band
26
25
27.7
28
29
II) Experiment of subject 2
a) S002r02cz: eyes closed
Situation of alpha band
Non smooth
Non smooth
Smooth
smooth
smooth
b) s002r01cz: eyes open
Fig.2-1: PSD of CZ
a) S002r02fz: eyes closed
b) s002r01fz: eyes open
Fig.2-2: PSD of FZ
a) S002r02pz: eyes closed
b) s002r01pz: eyes open
Fig.2-3: PSD of PZ
a) S002r02p3: eyes closed
b) s002r01p3: eyes open
Fig.2-4: PSD of P3
a) S002r02p4: eyes closed
b) s002r01p4: eyes open
Fig.2-5: PSD of P4
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5. Samaneh Valipour et al Int. Journal of Engineering Research and Applications
www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 1( Version 1), January 2014, pp.154-159
Channel
Cz
Fz
Pz
P3
P4
Table 4: Output of experiment for subject 2
Maximum of amplitude in alpha
band
23.5
21
29.5
27.5
29.5
III) Experiments of subject 3:
a) S003r02cz: eyes closed
Situation of alpha band
Non smooth
Non smooth
Smooth
smooth
smooth
b) s003r01cz: eyes open
Fig.3-1: PSD of CZ
a)S003r02fz: eyes closed
b) s003r01fz: eyes open
Fig.3-2: PSD of FZ
a)S003r02pz: eyes closed
b)s003r01pz: eyes open
Fig.3-3: PSD of PZ
a) S003r02p3: eyes closed
b) s003r01p3: eyes open
Fig.3-4: PSD of P3
a) S003r02p4: eyes closed
b) s003r01p4: eyes open
Fig.3-5: PSD of P4
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6. Samaneh Valipour et al Int. Journal of Engineering Research and Applications
www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 1( Version 1), January 2014, pp.154-159
Channel
Cz
Fz
Pz
P3
P4
Table 5: Output of experiment for subject 3
Maximum of amplitude in alpha
band
25
24
29.5
29.5
27.5
As is appears in figures and mentioned in
table 3,4 and 5 we can see for detection alpha band
parietal channels is best because of biggest amplitude
. Also we can see minor pick is very less in these
channels against CZ and FZ channels.
It is clear from graph that there is no any
alpha dominant frequency with open eyes in any
subject.
V.
Conclusion
With analogy of 5 channels in 3 subjects, it
is achieved that alpha band is dominant with closed
eyes in PZ, P3 and P4 channels as regards with CZ and
FZ, also CZ channel some time shows alpha rhythm.
With open eyes alpha band is suppressed. Intensity of
this band for any subject is different. Hence we can
conclude alpha rhythm is dominant in alert person
with closed eyes and back of head is good place for
showing that. If eyes are open in recording time alpha
wave is removed or amplitude of that is attenuated.
References
[1]
[2]
[3]
Steven Dowshen, MD. September (2010)
http://kidshealth.org/parent/general/sick/eeg.
html.
D Puthankattil Subha, Paul K Joseph,
Rajendra Acharya U, Choo Min Lim,
EEG signal analysis: a survey. Journal of
Medical Systems, 34:195–212 (2010).
Palva, S. and Palva, J.M., New vistas for afrequency band
oscillations,
Trends
Neurosci.
(2007),
doi:10.1016/j.tins.2007.02.001.
www.ijera.com
[4]
[5]
[6]
[7]
[8]
[9]
Situation of alpha band
Non smooth
Non smooth
Smooth
smooth
smooth
Ulrich Kraft. Train Your Brain-Mental
exercises with neurofeedback may ease
symptoms of attention-deficit disorder,
epilepsy and depression--and even boost
cognition in healthy brains. Scientific
American. (2006).
Arnaud Delorme, Hilit Serby, and Scott
Makeig, The EEGLAB Tutorial,
http://sccn.ucsd.edu/eeglab/eeglabtut.html
O. A. Padierna Sosa, Y. Quijano, M. Doniz,
J. E. Chong Quero Development of an
EEG signal processing program based on
EEGLAB ,PAHCE ,199-202(2011)
Md. Shahedul Amin, Md. Riayasat Azim,
Tahmid Latif, Md. Ashraful Hoque
Spectral Analysis of Human Sleep EEG
Signal (2010) 2nd International Conference
on Signal Processing Systems (ICSPS).
S. Deivanayagi et al, Spectral Analysis of
EEG Signals during Hypnosis International
Journal of Systemics, Cybernetics and
Informatics (ISSN 0973-4864).
A.L. Goldberger, L.A.N Amaral, L. Glass,
J.M. Hausdorff, P.Ch. Ivanov, R.G.
Mark, J.E. Mietus, G.B. Moody, C.-K.
Peng,
H.E.
Stanley.
PhysioBank,
PhysioToolkit, and PhysioNet: components
of a new research resource for complex
physiologic
signals.
Circulation
101(23):e215-e220 [Circulation Electronic
Pages;
http://circ.ahajournals.org/cgi/content/full/10
1/23/e215]; (2000) (June 13).
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