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Proceeding o/the 6th International Symposium on Mechatronics and
its Applications (ISMA09), Sharjah, UAE, March 24-26,2009
DETECTION OF EYE BLINKS FROM EEG SIGNALS FOR HOMELIGHTING SYSTEM
ACTIVATION.
Mohd Shaifulrizal b Abd Rani
Universiti Teknologi MARAFaculty of Electrical Engineering
UiTM, Shah Alam 40450, [email protected]
ABSTRACT
The improvement in the development of lighting system has
notconsidered eye blinking activity as a parameter to activate
thesystem. In addition, the use of eye blink extracted from
EEGsignal has not been investigated. This study examines a
suitableeye activity for activating home lighting system and then
detectsthe occurrence of this activity from EEG signals. Two types
of eyeactivity were analysed; eye blinks and eye movements. It
wasfound that EEG signal obtained from voluntary eye blinkcondition
produces clearer signals with larger amplitude than thatobtained
from eye movements. An efficient algorithm fordetecting the
occurrence of eye blinks was then developed and itsperformance was
investigated. Using this algorithm, the requiredeye blinks could be
detected successfully from 85% of therecords. Results from this
study show that with proper filteringand accurate detection, the
eye blinks could be used for activatinghome lighting system.
1. INTRODUCTION
Lighting system is one of the most important home
appliances.Most of the home lighting systems are activated manually
usingswitches. Nowadays, a lot of improvement has been made in
thedevelopment of lighting system, for example, the use of
remotecontrol to activate the lighting system. Besides this,
aprogrammable controller has been introduced to control the
timingof the lighting system which will obviously make our life
mucheasier.
Activating home lighting systems manually using switchesmay be
difficult to be performed by some disable persons. Eventhough a
remote control to activate the lighting system isavailable, but
this method is not possible to be used by individualswith motor
paralysis, armless and who cannot speak. In addition,the
programmable lighting system may not be the solution to
thisproblem.
Various methods of controlling electronic devices without theuse
of hands have been investigated by many researchers, for
ex~mples sip-and-puff: electro-oculogram (EOG signals),
lightemItter and others [1-3]. In our study, the use of the EOG
signalsto activate home appliances has also been studied [4]. Since
EOGsignals may not be suitable to be used when a person is not
facingthe system, a method that is more flexible has to be
investigated.
It is believed that eye blink is one of the mechanisms that
canhelp disable people to do their everyday routines. The eye
Wahidah bt. Mansor
Universiti Teknologi MARAFaculty of Electrical Engineering
UiTM, Shah Alam 40450, [email protected]
blinking activity can be detected from EEG(electroencephalogram)
signal via a brain computer Interface(BCI) which is a device that
allows people to communicatewithout using their mouths or hands
[5-7]. Since 1960's, manyresearchers have studied the use of brain
to directly control amachine. Most attempts ended with failure
because the technologywas not matured enough at that time.
Nowadays, the BCI-relatedstudies using various technologies have
emerged. By focusing onvery specific areas of brain activity, such
as motor function usingappropriate digital signal processing
technique, it is possible toextract useful information from EEG
signals [6, 8].
This study analyzes EEG signals obtained from variousconditions
to identify a suitable eye activity that can be used toactivate
home lighting system. Once the eye activity is found, analgorithm
is developed to examine its performance in detectingthe occurrence
of this activity.
2. EYE BLINKING ARTIFACTS IN EEG SIGNALS
Eye blinks are typically characterized by peaks with
relativelystrong voltages. They are often located by setting a
threshold andclassifying as eye blinks for all activity exceeding
the thresholdvalue. There is also certain variability in the
amplitude of thepeaks of a specific individual, more variability
between differentsubjects [9]. Eye blinks can be classified as
short blinks if theduration of blink is less than 200ms or long
blinks if it is greateror equal to 200ms [5].
Eye blinks can be classified into three types:
reflexive,voluntary and spontaneous [6]. The eye blink reflexive is
thesimplest response and does not require the involvement of
cortical
st~c~ures. In contrast, voluntary eye blinking (Le.
purposelyblInkIng due to predetermined condition) involves multiple
areasof the cerebral cortex as well as basal ganglion, brain stem
andcerebella structures [10]. Spontaneous eye blinks are those with
noexternal stimuli specified and they are associated with the
psycho-physiological state of the person [11].
Artifacts in EEG signals typically are characterized by
highamplitudes, which clearly hinder the analysis of the
recordings. Insome cases, eye blinking artifacts are a nuisance, as
EEG datarecorded with eyes open may be fraught with this kind of
artifacts.However, in this study the eye blinking artifacts may be
usefuland required as a parameter for activating home lighting
system.
ISMA09-1978-1-4244-3481-7/09/$25.00 2009 IEEE
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Proceeding ofthe 6th International Symposium on Mechatronics and
its Applications (ISMA09), Sharjah, UAE, March 24-26,2009
3. METHOD 4. RESULTS AND DISCUSSION
The EEG signal was recorded by placing three electrodes
onsubject's frontal, earlobe and occipital as shown in Figure 1.
Theelectrical conductivity jelly was applied to each electrode and
thesubject's skin was cleaned with alcohol prep pads before
placingthe electrodes. The KL-72001 main unit and KL-75004
EEGmodule which was connected to a computer via a RS-232 cablewas
used to record the EEG signals.
The normal EEG signal of a relaxed patient is shown in Figure3.
The signal consists of beta waves which lie in the frequencyrange
of 13 to 22 Hz and spontaneous eye blinks which is below 5Hz. The
negative amplitude shows the eyelids closure andpositive value
shows the opening of eyelids. The amplitude ofspontaneous eye blink
is in the range of -4 V to 3 V and itsduration is less than 400
ms.
Figure 1. Connection ofelectrodes on subject
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Figure 3. Normal EEG signal (relax condition) and
itsspectrogram.
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Figure 4 shows the eye blinks for the voluntary eye
blinkingcondition. This EEG signal comprises eye blinking
waveforms(below 5 Hz) that have larger amplitude and longer
duration (400-500 ms) compared to that obtained from spontaneous
eye blink. Itwas also found that three continuous eye blinks (with
a duration of1.5 to 2.5 seconds between eye closure and opening)
are suitableto be used for activating lighting system as they are
not presentwhen the subject is in relax condition (see Figure 3).
The durationbetween the first cycle of eye opening and closure and
the secondcycle is 3 to 4.5 seconds.
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In this study, the EEG signals were recorded from 20 subjects
andfour conditions were considered; relax, voluntary eye blink,
eyeclosure, eye movements (left, right, upward and downward) andone
eye blink. In the voluntary eye blinking case, the eyes areclosed
and opened three times for every 2 seconds and 3
secondsrespectively. The EEG signals obtained from the eye
activitieswere compared with that obtained when the subject was in
relaxcondition. This was done to identify the best eye activity
that canproduce clear signal with high amplitude. The effect of
variouseye blinks and duration of blink on the EEG signals were
alsostudied. The EEG signal was sampled using a sampling
frequencyof 50Hz and analyzed using Short-time Fourier transform.
Thesignal was then filtered using a low pass filter with a cut
offfrequency of 5 Hz to remove high frequency signal.
Among all the conditions mentioned above, the voluntary eyeblink
produced high amplitude EEG signals and was used throughout the
study. Since the main concern of this extraction process isto
detect only the occurrence of the eye blink, a threshold value
of4J.1V was used to preserve the peak amplitude and remove thelower
amplitude of EEG signals. Figure 2 shows the process offiltering,
removing low amplitude signal and detecting the eyeblinks from EEG
signals. The eye blinking information wasextracted using an
algorithm written in MATLAB. This algorithmdetected three adjacent
local maxima and minima within a specificamplitude and range that
can differentiate between spontaneouseye blink.
Figure 2. Basic block diagram ofEEG signal processing
Figure 4. EEG signal and its spectrogram for voluntary
eyeblinking condition.
The EEG signal obtained when the eyes moved to the rightand left
is shown in Figure 5. This signal contains a lot of artifacts
ISMA09-2
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Proceeding o/the 6th International Symposium on Mechatronics and
its Applications (ISMA09), Sharjah, UAE, March 24-26,2009
5 .
10 .
caused by spontaneous eye blinking and eyelids movement as
theeyeball moved. Therefore, signals obtained from these
eyemovements are not suitable for home lighting system activation
asthe occurrence of eye movements is difficult to detect.
Similarly,the EEG signals obtained from the upwards and
downwardmovements consists of noise which covers the
requiredinformation to be extracted.
I
Threshold SignalI I I
5 .
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500 1000 1500 2000 2500
Sam~es
Figure 7. EEG signal after applying threshold
250020001500Sam~es
1000500
Figure 8. Pulses obtainedfrom eye blinking detectionindicating
the switching states.
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-
I I I I I1
o
-0.5
Desired Output SignaJ
0.5
1.5
1800160014001200800600400200 1000~
Figure 5. EEG signal obtained when the eyes are moved to
theright and left
-80
Figure 6 shows the EEG signal after filtering. It is obviousthat
the signals above 5 Hz have been removed leaving only veryclear eye
blinking signals. Other artifacts such as 50 Hz powerline
interference and noise have also been eliminated usinganalogue
filtering provided by the EEG instrument. Figure 7 andFigure 8 show
the EEG signals after applying threshold and thepulses obtained
from eye blink detection respectively. The pulse isgenerated when
three continuous eye blinking (with the conditionmentioned above)
occur in the EEG signal which represent theactivation of the switch
whereas the zero state indicates the switchis turned OFF.
After applying the eye blinking detection algorithm to
twentysamples of EEG signals, it was found that the eye blinks can
bedetected successfully from 85% of the records. Good
detectionresults could not be obtained from some of the records.
This is dueto noises generated from improper electrode placements
and lowamplitude of EEG signals when the subject's eyes were not
fullyblinked.
Noise FiRered Signal 5. CONCLUSIONS
10 .
8 .
6 .
4 .
-6
.........................................................................................................................
-8 .
The analysis of EEG signals obtained from four conditions;
relax,voluntary eye blink, eye closure, eye movements (left,
right,upward and downward) and one eye blink and the detection of
eyeblinks have been described in this paper. The EEG
signalsobtained from voluntary eye blink produced clear signals
whichcould be easily detected. The eye blinking technique that can
beused for activating home lighting system is by closing andopening
the eyes three times for every 2 seconds and 3 secondsrespectively.
The simulation results showed that eye blinks can bedetected
successfully from the EEG signals using the proposedalgorithm with
the success percentage of approximately 85%.Therefore, the proposed
eye blinking technique can beimplemented to activate home lighting
system.
250020001500Sam~es
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o
Figure 6. Noise-filtered EEG signal
ISMA09-3
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Proceeding ofthe 6th International Symposium on Mechatronics and
its Applications (ISMA09), Sharjah, UAE, March 24-26,2009
6. REFERENCES
[1] Ding, Q., Tong, K., Li, G., "Development of an
EOG(Electro-Oculography) Based Human-Computer
Interface",Proceedings of IEEE EMBS, Shanghai, China, 2005,
pp.6829-6831.
[2] Kumar, D. and Poole, E., "Classification of EOG for
HumanComputer Interface", Proceedings of IEEE EMBSIBMES,USA, 2002,
pp 64-67.
[3] Breau, F., Marsden, B., McCluskey, 1., Ellwood, R. 1.,
lewis,J., "Light Activated Position Sensing Array for Persons
withDisabilities", Proceedings ofIEEE on Bioengineering, 2004,pp
204-205.
[4] Harun, H. and Mansor, W., "EOG Signal Detection for
HomeAppliances Activation", IEEE Colloquium on SignalProcessing and
Its Applications, Kuala Lumpur, Malaysia,2009, in-press.
[5] Manoilov, P. P. "EEG Eye-Blinking Artifacts PowerSpectrum
Analysis", Proceedings of Int. Con. on ComputerSystems and
Technologies, CompSysTech '06, V. Tamovo,Bulgaria, 15-16 June 2006,
pp. IIIA.3-I-IIIA.3-5.
[6] Kr6lak, A., and Strumillo, P. "Vision-Based Eye
BlinkMonitoring System for Human-Computer Interfacing",Institute of
Electronics, Technical University, Lodz, Poland.
[7] Yoo, K., Basa, T., and Lee, W. "Removal of Eye
BlinkArtifacts from EEG Signals Based on
Cross-Correlation",Proceedings of IEEE Con. on Convergence
InformationTechnology, 2007, pp. 2005-2008.
[8] Funase, A., Vagi, T., Kuno, T., Uchikawa, T. "Prediction
ofeye movements from EEG ", School of Engineering,
NagoyaUniversity, Furo-cho, Chikusa,-ku, Nagoya 464-603, Japan.
[9] Delsanto, S., Lamberti, F., Montrucchio, B. "AutomaticOcular
Artifact Rejection based on Independent ComponentAnalysis and
Eyeblink Detection ", Dipartimento diAutomatica e Informatica,
Politecnico di Torino, Italy, March2003.
[10] Glass, K. A., Frishkoff: G. A., Frank, R. M., Davey C.,
Dien,1., Malony, A. D., Tucker, D. M. ''A Framework forEvaluating
ICA Methods of Artifact Removal fromMultichannel EEG", Institute of
Electronics, TechnicalUniversity, Lodz, Poland.
[11] Hirokawa, K., Yamada, F., Dohi, I. and Miyata, Y. "Effect
ofgender types on interpersonal stress measured by blink rateand
questionnaires: Focusing on stereotypically sex-typedand
androgynous types" - Social Behaviour and Personality,2001.
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