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Abstract-- Electroencephalogram (EEG) signal has been found to

be the most predictive and reliable indicator in wake-sleepresearch. It is a real time signal that reflects the brain states of asubject including the alertness. However the study of wake-sleepcondition using EEG signal is difficult due to the complexity ofthe EEG signal itself. The exact underlying dynamics of the EEGdata is still questionable. EEG signal varies from one individualto another and has an inter variability in the same physiological

state. It is hard to compare the EEG to the specific pattern ofindividual or situation. This paper tries to investigate the use ofUMACE in distinguish between awake and sleep state of a

subject. Normal EEG data from individual is used as an input inbuilding UMACE filter. From the result, we find UMACE hasthe capability to distinguish awake and sleep state of a subject.

Index TermsElectroencephalogram, Uncostrained MovingAverage Correlation Energy (UMACE), Wake-sleep

I. INTRODUCTION

EG is one of the well known method applied in extracting

signal from the brain. Researches in wake-sleep, emotion

and epilepsy detection mostly utilize the information of the

EEG signal. It is well known that EEG signal is a very

complex signal and the exact underlying conditions that

produce the signal is still uncertain. Two common drawbacksof EEG as inputs are inter-trial variability and inter-individual

variability. Moreover, EEG has no specific pattern to indicate

ages and conditions. It matures with the age of the person,

therefore infant and adolescence will have different patterns of

EEG signal. The signal is altered as the person grows older.

Since maturation depends on age, it is difficult to make a

specific comparison (distinct physiological state).

In wake-sleep research, EEG is a known method that can

depict the changes in brain states. The changes of the brain

state for wake-sleep can be detected in the frontal area [1].

The most prominent waves that showed a consistent pattern

and occurrence are beta waves, alpha waves, theta waves and

delta waves. In detection sleep onset, beta waves and alpha

waves play an important rule in early stage of drowsiness and

R. Ghafar is with Universiti Kebangsaan Malaysia, 43600 Bangi Selangor,Malaysia (e-mail: rosni@vlsi.eng.ukm.my).

N. Md. Tahir is with Universiti Teknologi Mara, 40500 Shah Alam,Selangor, Malaysia (e-mail: norita_tahir@yahoo.com)

A. Hussain is with Universiti Kebangsaan Malaysia, 43600 Bangi

Selangor, Malaysia (e-mail: a ini@vlsi.eng.ukm.my)S. Abdul Samad is with Universiti Kebangsaan Malaysia, 43600 Bangi

Selangor, Malaysia (e-mail: sal ina@vlsi.eng.ukm.my)

early stage of sleep. The description of EEG changes in sleep

stage is as in Table 1 [2]. Some disadvantages of utilizing

EEG as inputs for the sleep state are; in term of alpha wave,

up to 10% of the population does not show alpha activity [3]

and the maximum accuracy that can be achieved when using

manual scoring by two or more neurologists are around 70 %

to 80 % only [4]. Therefore, a computer assisted diagnosis is

needed to increase the accuracy and further served as a

guideline to diagnose wake sleep stage. The changes during

transition of awake to sleep stage are individually differ from

one to another person. Therefore it is hard to compare EEG toa specific pattern. In order to develop a technique that can be

used for all EEG data, we try UMACE method using

individual EEG data as an input. Three seconds segment is

taken from the normal segment as a training set for each

individual patient.

TABLE1SCORING FOR WAKE-SLEEP USING EEG

Stage EEG

Awake Low-voltage, desynchonised, 10Hz alpha with eyes

shut

Stage 1 Low-voltage, desynchonised, no alpha. Some theta

bursts or vertex spikes

Stage 2 Low-voltage, desynchonised, with phasic 13-15Hz

spindles and K complexes

Stage 3 As stage 2, but increasing slow wave (50% of record

Stage REM Low-voltage, desynchonised, sometimes preceded by

3-7Hz saw-tooth waves.

This paper investigates the used of UMACE filter todiscriminate two conditions namely, awake and sleep stateusing EEG as input data. Recently, UMACE filter have beenexplored and they have evolved into very effective algorithms

for pattern recognition applications specifically biometrics

verification [5]-[8]. For example, [8]-[9] proved that the

advanced correlation filter such as MACE and UMACEoffered good matching performance in the presence of

variability such as different facial expressions andillumination. These findings suggest that the UMACE could

be explored to process the EEG signal with inter-trial and

inter-individual variability.

Furthermore, UMACE is chosen in this study due to its

capability to function in a limited memory device, ease of

installation and produces fast results [9]. In real time, EEG

EEG Analysis of Wake-sleep Data using

UMACE filterR. Ghafar, N. Md. Tahir, A. Hussain, Member, IEEE& S. A. Samad, Sr. Member, IEEE.

E

The 5h

Student Conference on Research and Development SCOReD 200711-12 December 2007, Malaysia

1-4244-1470-9/07/$25.00 2007 IEEE.

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signal changes rapidly over time, therefore an efficient

processing technique is needed for first-rate results.

II. MACE AND UMACE FILTERS

Minimum Average Correlation Energy (MACE) filter is an

evolution from matched filtered that is design to produce

optimal detection from a known reference images in the

presence of Gaussian white noise. Matched filter performanceis easily deteriorates by the scale changes, rotation and pose of

the image. Synthetic discriminant function filter and equal

correlation peak filter is created to overcome the problem arise

in using matched filter. This filtered used several training

images to create a single correlation filter. Synthetic

Discriminant Function (SDF) produced a pre-specified peak

known as peak constrain. The peak constrains lead to

miscalculation when the sidelobes are larger than the

controlled origin. Thus, to reduce large sidelobes the MACE

and UMACE filters are designed. MACE filter minimizes the

average correlation energy of the training image while

constraining the correlation output at the origin to a specific

value for each training images. On the other hand, UMACE

minimizes the average correlation output while maximizing

the correlation output at the origin..

The equations for MACE and UMACE are as given in (1)

and (2) respectively:

h=D-1X(X+D-1X)-1u (1)

h=D-1m (2)

whereD is a diagonal matrix with the average power spectrum

of the training image placed along diagonal elements; Xconsists of the Fourier transform of the training imageslexicographically re-ordered and placed along each column; u

is a column vector containing the desired correlation output at

the origin for each training images and m is a column vector

containing the mean of the Fourier Transforms of the training

images. Detail derivation of both MACE and UMACE

equations can be found in [8]. Studies have shown that theMACE filter has no built-in immunity to noise and it is often

excessively sensitive to intra-class variations as compared to

UMACE filter [7]-[9]. Therefore only the latter will be

adopted in this study.

III. TECHNICAL WORKPREPARATION

In this study, data was recorded using the Biopac systems

and the AcqKnowlegde software (Model MP 100 Data

Acquisition Systems; Biopac Systems Inc, Goleta, CA; using

AcqKnowledge 881 Version 3.1.2 software). The recording

was done at sampling rate of 256 Hz. The subjects involved

aged between 21 to 35 years. Several steps were taken in order

to determine better result such as the avoidance of applying

any type of hair gel or conditioners for effective contact

between the scalp and electrodes and all subjects are advised

from taking any form of medication prior to recording.

Besides, they are also recommended to reduce their coffee

intake and deprive their sleep the night before in order to get a

quick sleep data during recording. Subjects were seated in a

sound-controlled air conditioning room with dim lighting to

provide comfortable sleeping condition for the recording

purpose. During recording, subjects were also requested to

perform brain related activities namely reading and some

simple mental calculation.

Eighteen surface electrodes were placed at specific location

all over the scalp based on the 10-20 systems. The electrodes

are connected by wires to an EEG machines. Data taken using

Biopac system was converted into text file prior to processing

in the MATLAB environment. The EEG data is filtered using

the low-pass filter that allows only frequency below 50Hz to

be processed; this helps to discard the 50Hz noise that was

generated during the recording. Since the component that

triggered certain situation is still unknown, we prefer to use

only filtered data as an input to the UMACE filter. The further

processed data are rarely used because certain information

might be missing and unavailable [10].

Typically, UMACE filters utilized images as their inputs.

Image is a 2D data where as EEG signal is a one dimensional

(1D) data. Therefore, embedding method has been applied to

the EEG data in order to increase the EEG data

dimensionality. Next, UMACE is applied to the filtered and

embedded EEG data. Three consecutive seconds are taken

from the sleep portion data and used as the training set. Fig. 1

depicts the overall system using UMACE filter for wake sleep

detection.

Fig. 1. Application of UMACE filter for wake sleep detection

Fourier transform of the embedded training data is used to

build a synthesized correlation filter; the UMACE filter. The

Fourier transform of the embedded test image is then cross-

Rearrange into 2D

FFT

CorrelationFilter

Filter

Design

IFFT

Training data

Test data

FFT

FFT

FFT

Correlation Plane

ROI

Sum of ROI

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correlated with the synthesized UMACE filter and produced

the correlation output. Most of the previous research on

advance correlation filters used the Peak to side lobe ratio

(PSR) measurement from correlation output as an indicator for

discrimination or classification. However, PSR value was

found to be unreliable for the case of EEG as input. These are

due to the fluctuations and inconsistency of the EEG data.

However, there are consistent changes in the correlation plot

of UMACE filtered. As depicted in Fig. 2, the correlation plotfor sleep portion is almost black with pixel values is mostly

near zero. In contrast, the wake portion correlation output

produces a tiny gray pixel in the center of the plot as

illustrated in Fig. 2b.

The difference which is located in the center of the plot is

difficult to visualize. Fig. 3 is used to reveal the variation in

the plot between sleep and wake stage. The differences

between sleep and wake states can be seen more prominently

in the inversion plot as shown in Fig. 3. The changes in the

middle of the correlation output plot are consistent for three

subjects. Instead of using PSR, the sum of the pixel value is

calculated for the middle area in order to producedistinguishable result. The middle area is called ROI. Then,

the sum of ROI is plotted as indicated in Fig. 3, Fig. 4 and Fig.

5 respectively. Preliminary results demonstrated the existence

of very small differences in distinguishing the awake and sleep

states. The result is further smoothed using moving average

filter to improve the result and enhance the differences

between the two states.

(a) sleep (b) awake

Fig. 2. Correlation output

(a) sleep (b) awake

Fig. 3. Correlation output (inversion)

IV. RESULTS

In this section, the results of the UMACE filter as depicted

in Fig. 4, Fig. 5 and Fig. 6 are examined. The first plot (top) is

the normalized sum of ROI (pre-filtered sum of ROI). The

second plot (bottom) is the smoothen sum of ROI plot using

moving average filter.

Fig. 4. Subject 1

Fig. 5. Subject 2

.

Awake AwakeSleep

Awake AwakeSleep

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Fig. 6. Subject 3

It can be seen that the pre-filtered sum of ROI results

showed extreme fluctuation. After filtering, the processedEEG data (i.e. the sum of ROI) has less fluctuation and thesleep region for all subjects can be determined easily since the

fluctuation has reduced to values between 0 to 0.3. The sleep

region has normalized magnitude of 0.3 or less. The plot

showed consistent results in the sleep region and enormous

fluctuation in the wake region. As for the wake region, the

fluctuation is greater and the magnitude varies from 0 to 1.The preliminary findings of the use of sum of ROI obtained

from UMACE method can be applied as guidelines orreferences in building an application for wake-sleep detection.

Fluctuations occurred in the wake region are due to

movement, conversation and mental task activities performed

during recording and therefore are uncontrollable. In thisstudy, subjects were not restraint as they are allowed to move

slightly in order to provide greater comfort in finding the best

position for them to get a good sleep. Besides, with or without

movement, it is natural for the EEG signal to have fluctuations

presence.

V. CONCLUSIONSIn conclusion, the UMACE method has been proven

applicable to detect the wake sleep condition in EEG data. The

changes of the correlation plot are vital and therefore are

considered instead of the normal PSR ratio. From the result, it

can be concluded that the ROI plot can be used to classify the

wake and sleep stage of a subject. Sleep stage show smaller

fluctuations in the form of ripples as contrast to the wake state.

These characteristics can be utilized as an aid to interpret the

EEG signal. Further work has to be done to improve the

detection and automate the process in order to facilitate in the

wake-sleep scoring process.

VI. ACKNOWLEDGMENT

The authors expressed their gratitude to Miss Fatimah

Abdul Hamid for the data used in this study.

VII. REFERENCES

[1] C. Cajochen, R. Foy, D. Dijk, (1999) Frontal Predominance of a

Relative Increase in Sleep Delta and Theta EEG Activity after SleepLoss in Humans, Sleep Research Online, 2(3), pp:65-69. Availabel:http://www.sro.org/1999/Cajochen/65/65

[2] J. Empson, Human Brainwaves. The Psychological Significance of theElectroencephalogram, London, Macmillan Press Ltd, 1986, p. 70.

[3] U. Svenson, Blink behaviour based on drowsiness detection method

development and validation, Linkopings Universitet, Tecg. Rep. LiU-IMT-EX--04/369--,Sept. 2004.

[4] V. Gerla, L. Lhotska, V. Krajca, and K. Paul, Multichannel Analysis of

the Newborn EEG Data.In Proc. The Int. Special Topic Conference onInformation Technology in Biomedicine (ITAB 2006). Available at :

http://medlab.cs.uoi.gr/itab2006/proceedings/EEG%20Analysis/52.pdf

[5] S. A. Samad, D. Athiar Ramli & A. Hussain., Person IdentificationUsing Lip Motion Sequance, B. Apolloni et at (Eds). Pp 839-846, 2007.Springer-Verlaq Berlin Heidelberg

[6] S. A. Samad, D. Athiar Ramli & A. Hussain., Lower Face VerificationCentered on Lips using Correlation Filters, Information TechnologyJournal, 2007 (in press)

[7] N. Md. Tahir, A. Hussain & S. Abdul Samad, On The Use ofAdvanced Correlation Filters for Human Posture Recognition, Journalof Applied Science (in press)

[8] M. Savvides, B. V. K. Vijaya Kumar and P. K. Khosla, FaceVerification using Correlation Filters, in Proc. of the Third IEEE

Automatic Identification Advanced Technologies, pp. 56-61, 2002

[9] M. Savvides and B. V. K. Vijaya Kumar, Effificient Design of

Advanced Correlation Filters For Robust Distortion-Tolerant FaceIdentification, inProc. IEEE Int. Conf. On Advanced Video and Signal

Based Surveillance(AVVS), pp. 45-52, 2003[10] C. K. Ng, M. Savvides, and P. K. Khosla, Real-Time Face Verification

Systems on a Cell-Phone Using Advanced Correlation Filters, in Fourth

IEEE Workshop on Autimatic Identification Advanced Technologies(AutoID05), pp. 57-62.

[11] D. Sommer, M. Chen, M. Golz, U. Trutschel, and D. Mandic, Fusion ofState Space and Frequency-Domain Features For Improved MicrosleepDetection (W. Duch et al, Eds.): ICANN 2005, LNCS 3697, pp. 753-759.

VIII. BIOGRAPHIES

Rosniwati Ghafar obtained her Bachelor of Engineering and Mathematics

from Vanderbilt University, USA in 1997. In 2004, she received her Masterdegree from Universiti Sains Malaysia. Now workingtowards Ph.D degree in signal processing field at

Faculty of Engineering, Universiti KebangsaanMalaysia.

Nooritawati Md Tahir is a lecturer at UniversitiTeknologi Mara (UiTM) since December 1991. She

obtained her BEng in Electrical Engineering from

UiTM in 1988 and MSc (Electronics &Communication) in 1991 from the University ofLiverpool, UK. Currently, she is working toward her

PhD degree in the field of image processing, patternrecognition and computational intelligence at theFaculty of Engineering, Universiti Kebangsaan

Malaysia.

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Aini Hussain (M97) received the B. Sc. inElectrical Engineering, M.Sc. in Control Engineering

and Ph.D from Louisiana State University, UMIST,and Universiti Kebangsaan Malaysia, respectively.She is a professor at the Department of Electrical,

Electronic & Systems Engineering, UniversitiKebangsaan Malaysia. Her research interests includesignal processing, pattern recognition and

computational intelligence.

Salina Abdul Samad (M95-SM07) received theBSEE and Ph.D in electrical engineering from

University of Tennessee and University ofNottingham, respectively. She is a professor at theDepartment of Electrical, Electronic & Systems

Engineering, Universiti Kebangsaan Malaysia. Herresearch interests include digital signal processing,filter design and multimodal biometric system.