Appl. Math. Inf. Sci. 7, No. 2, 809-816 (2013) 809

Applied Mathematics & Information SciencesAn International Journal

c⃝ 2013 NSPNatural Sciences Publishing Cor.

Automatic Removal of Ocular Artifact from EEG withDWT and ICA MethodMingai Li∗, Yan Cui and Jinfu Yang

College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124,China

Received: 19 Sep. 2012; Revised 21 Sep.2012; Accepted 9 Dec.2012Published online: 1 Mar. 2013

Abstract: Ocular artifact (OA) is one of the main interferences in electroencephalogram (EEG) recordings. It appears as a big pulseand has a strong impact to EEG signals. To overcome OA interference in EEG data, a novel automatic method of OA removal, denotedas DWICA, was proposed in this paper. In DWICA, the discrete wavelet transform (DWT) is applied to every recorded signal to obtainmultiple scale coefficients. Then the independent component analysis (ICA) algorithm is used, and its input is the coefficients connectedin series. Thus the independent components are acquired quickly in wavelet domain. The criterion of angle cosine is introduced torecognize ocular artifact, and the corresponding component is set to zero. Furthermore, the artifact free components are projected tooriginal electrodes with inverse ICA algorithm. Finally, DWT is inversed to obtain the artifact free brain signals. Quantitative studiesabout suppression of OA distorting the underlying cerebral activity and accurate evaluation on denoising effect of DWICA are finishedin this paper. Experiment results show that DWICA is preferable and effective in automatic OA correction. Meanwhile, DWICA ispowerful in noise immunity and fast in convergence rate, and it provides a preferable method for EEG preprocessing on-line.

Keywords: Ocular artifact, electroencephalogram,independent component analysis,preprocessing,criterion of angle cosine

1. Introduction

Electroencephalogram (EEG) is a biological signal reflect-ing complex activities of brain. It plays an important rolein human brain research, disease diagnosis, rehabilitationengineering and so on. However, EEG is weak and time-varying, and it is easily affected by other noises. Thereforevarious artifacts are formed during EEG signal recordings.Electrooculogram (EOG) is one of the main interferencesin EEG, which appears in EEG recordings randomly as abig pulse and forms ocular artifact (OA). OA brings aboutmuch difficulty in EEG signal processing, and even affectsits analysis and recognition [1]. So it is very important toremove ocular artifact without losing any information inEEG signal preprocessing.

Now there are 4 main methods of OA removing fromEEG: (1)Artifact Abstraction [2]. The method assumes thatEEG and EOG recordings are in accord with linear combi-nation and uncorrelated with each other. So ocular artifactcan be estimated and removed in proportion from EEG.This method has explicit physical meaning and is appliedearly. In fact, there is actually mutual influence and bidire-

ctionality between EEG and EOG, artifact abstraction maylose some important information in removing EOG fromEEG. (2)Wavelet Transform (WT) [3]. The method is basedon the different statistical characteristics of signal and noiseafter wavelet transform. It is a time-frequency analysis met-hod, and it is particularly suitable for non-stationary sig-nals such as EEG. However, this method requires that thefrequency bands of signal and noise should not overlapeach other. In the overlapping bands of EEG and EOG, thedenoising effect is not quite good. Now some researchersare trying to combine wavelet transform and other meth-ods together in order to improve denoising effect. (3)Prin-ciple Component Analysis (PCA) [4]. In PCA, the signalis decomposed based on orthogonality criterion, and thenartifact is removed according to the contribution of eachcomponent. This method performs much better than arti-fact abstraction. But only the covariance matrix of signalis considered here, and high order redundant informationmay remain in the decomposed components [5]. (4)Inde-pendent Component Analysis (ICA) [6]. ICA is a signif-icant decorrelation method based on two and even higherorder statistical information, and it is actually an exten-

∗ Corresponding author: e-mail: limingai@bjut.edu.cnc⃝ 2013 NSP

Natural Sciences Publishing Cor.

810 Mingai Li, Yan Cui, Jinfu Yang: Automatic Removal of Ocular Artifact...

sion of PCA. So it has more advantage over PCA. Cur-rently ICA is perceived as a potentially robust and pow-erful method for the artifact removal in EEG and receivesan increasing attention. ICA separates the recorded EEGsignals into statistically independent sources, and then re-jects those responsible for artifacts. The majority applica-tions of ICA in EEG processing focus on the removal ofocular artifact. However, there are still several issues thatshould be addressed. Firstly, classical ICA model doesn’ttake other noises into consideration. In fact, EEG is easilydisturbed by various noises in the collection process, andthe separation performance of ICA algorithm is affectedseriously. Besides, ICA algorithm needs more iterationsto get the separation matrix, which is significantly timeconsuming and inefficient. Secondly, for the uncertaintyof ICA, it is rather difficult to decide which component ac-counts for artifact. The traditional semi-automatic methodis a combination of visual observation and topographicalmap of brain [7], but it is not desirable for real-time artifactsuppression. Joyce [8] and Flexer [9] used the correlationbetween reference EOG and each independent componentto detect the one responsible for ocular artifact. However,the determination of correlation threshold relies on someexperience, and the resolution of correlation coefficients isnot very high. Therefore, ICA is usually used as an offlinemethod for ocular artifact cancellation from EEG, and theresult needs to be improved.

Furthermore, clean EEG signals are difficult to obtainduring the actual collection process, and there are few stan-dard EEG data bases provided for OA research. So it ishard to find an accurate assessment for OA removal ef-fect. Most authors presented their results graphically, andfew quantitative performance indexes were given to evalu-ate denoising result. It is difficult to determine accuratelywhich method is better.

Based on discrete wavelet transform and independentcomponent analysis, a novel automatic removal method ofocular artifact, denoted as DWICA, was proposed in thispaper. In order to assess denoising effect of DWICA accu-rately, a mathematical model of EEG and EOG was builtaccording to their bi-directionality. Then the EEG exper-imental data contaminated with ocular artifact was con-structed, and quantitative performance indexes were com-puted to assess the denoising effect. Experiment resultshave shown that the signal to noise ratio of EEG is greatlyimproved by DWICA. This method is powerful in noiseimmunity and fast in convergence rate, and it will providea novel preferable idea for on-line preprocessing of EEGdata.

2. Basic principles

2.1. Discrete Wavelet Transform

Wavelet transform is a time-frequency analysis method onthe basis of Fourier transform [10]. The wavelet coeffi-

cients can reflect both the time and frequency domain in-formation of signal. Therefore, wavelet transform is widelyused in the processing of biomedical signal, especially suit-able for the non-stationary one such as EEG. The compu-tation speed of discrete wavelet transform (DWT) is veryfast, and it is desirable for real-time artifact suppressionin EEG. Moreover, the practical signals that need to beprocessed are discrete after sampling, so discrete wavelettransform is used widely.

For ∀ f (t) ∈ L2(R) the DWT is given as follows:

WTf ( j,k)=< f (t),ϕ j,k(t)>=∫ +∞

−∞f (t)ϕ j,k(t)dt, j,k∈Z.

(1)Where ϕ j,k(t) = 2−

j2 ϕ(2− jt − k) is the binary expansion

and shift of the mother wavelet function ϕ(t), j and k arethe shifts of frequency resolution and time respectively.ϕ j,k(t) is the conjugate of ϕ j,k(t).

The inverse discrete wavelet transform (IDWT) is de-fined as follows:

f (t) =∞

∑j=−∞

∞

∑k=−∞

WTf ( j,k)ϕ j,k(t), j,k ∈ Z. (2)

Mallat proposed the fast algorithm of wavelet analy-sis and reconstruction based on the pyramid algorithm inimage decomposition and reconstruction. The algorithmgives access to DWT and IDWT with double channel fil-ters on the basis of multi-resolution analysis, and it pro-vides a very convenient idea for its further application.

A L-level decomposition of signal f (t) is obtained withMallat algorithm, and the corresponding wavelet coeffi-cients are given by:

a j,k = ∑m∈Z

a j−1,mh0(m−2k), (3)

d j,k = ∑m∈Z

a j−1,mh1(m−2k). (4)

Where a j,k are the approximate coefficients of the j-th( j =1, · · · ,L) scale and d j,k are the detail, and a0,k = f (t). Themulti-resolution coefficients of the signal at each scale canbe obtained with the scale increasing gradually. h0(k) is thelow frequency filter while h1(k) is the high frequency one,and both of them are determined by the selected waveletbasis. The typical three level decomposition tree is shownin Fig.1 . Where A j is the approximate vector and D j isthe detail one of the j-th scale. Moreover, the decomposi-tion meets the following equation:

f (t) = A3 +3

∑j=1

D j. (5)

It is easy to conclude from the decomposition tree inFig.1 that the further decomposition is only for the low fre-quency band of signal, not for the high frequency band. Sothe signal f (t) can be divided into many sub-bands after

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Figure 1 Three level decomposition tree.

decomposition. The Mallat’s pyramid reconstruction algo-rithm is given as follows:

a j−1,m = ∑k∈Z

a j,kh0(m−2k)+d j,kh1(m−2k). (6)

The signal f (t) can be reconstructed with the scale jdecreasing gradually.

2.2. Independent Component Analysis

ICA is a recently developed method on the basis of blindsource separation. It has been applied to solve denoisingproblems in recent years and achieved a robust and pow-erful result [6]. The idea of ICA comes from the CentralLimit Theorem. A sum of random variables tends towarda gaussian distribution under the condition that their meanand variance have the same order. So, when the statisticallyindependent sources are mixed into a group of signals, itis necessary to estimate the nongaussian property of sepa-ration signals. Maximizing the nongaussianity can achieveseparation of the recorded signals.

The mathematic model of ICA is given as follows:

x(t) = A · s(t), (7)

y(t) =W · x(t). (8)

Where s(t)= [s1(t),s2(t), · · · ,sn(t)]T ∈Rn×M is n channelsof original sources that are real-valued, non-Gaussian dis-tributed, and statistically independent. M is the samplingpoint of each signal. x(t)= [x1(t),x2(t), · · · ,xn(t)]T ∈Rn×M

is n channels of observed mixtures. It is modeled as the re-sult of multiplying an n×n matrix (i.e. square matrix A) bys(t). Independent component analysis is to estimate orig-inal sources from the observed mixture x(t) while know-ing little about the mixing process as well as the matrixA. It is necessary to estimate the linear separation matrixW ∈Rn×n to recover a version y(t) to approach the originalsources s(t) as far as possible.

ICA is based on the assumption that all the originalsources are mutually independent. So, the objective func-tion is established and optimized to estimate the separateeffect of each independent component. The ultimate pur-pose is to obtain the latently independent sources. There-fore, the key of ICA is to establish an objective function for

estimating the independence of separate components andto find the corresponding separation algorithm. FastICAis good in performance and has a lot of advantages listedas follows: (1)Convergence speed of this algorithm is atleast quadratic, this means that the convergence rates ofFastICA is very high. (2)Different from other algorithmsbased on gradient, it is not necessary to choose the step pa-rameter, and it is therefore quite convenient to use. (3)Fas-tICA shares some advantages of neural network such asparallel and distributed computation. So it is simple in cal-culation and needs much less memory space. For all thereasons above, FastICA based on the negentropy is adoptedin this paper.

2.3. The criterion of angle cosine

The cosine of angle is often used in geometry to estimatethe similarity between two vectors. While in machine learn-ing, it is applied to measure the difference between twosamples. The criterion of angle cosine has higher resolu-tion than correlation coefficient. Currently it is widely usedin many fields such as the similarity measurement of fin-gerprint, the classification of text and spectrum etc. For theuncertainty characteristics of amplitude as well as polarityand order for each independent component in ICA, it isdifficult to decide which one accounts for ocular artifactand should therefore be set to zero. The criterion of anglecosine was introduced in this paper to estimate similaritybetween each independent component and the referenceEOG so as to recognize ocular artifact automatically.

Suppose that yi = [yi1,yi2, · · · ,yiM]T ∈ RM×1 is the i-thindependent component of EEG, and

xl = [xl1, xl2, · · · , xlM]T ∈ RM×1,

is the reference EOG. Here, M is the sampling point ofeach signal. The cosine of angle reads as follows:

cosθi =

M

∑q=1

yiqxlq√√√√ M

∑q=1

y2iq

M

∑q=1

x2lq

. (9)

It is obvious that cosθi belongs to [−1,1]. Because of theuncertainty of amplitude and polarity for each indepen-dent component, the absolute value of cosθi (i.e. |cosθi|)is chosen to estimate the similarity between each inde-pendent component of EEG and the reference EOG. Thelarger value represents more similarity of the correspond-ing component to the reference EOG.

3. Ocular Artifact Removal With DWICA

In 2003, Jafari combined wavelet transform and ICA methodtogether in fetal electrocardiogram extraction for the first

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812 Mingai Li, Yan Cui, Jinfu Yang: Automatic Removal of Ocular Artifact...

time [11]. This idea was further applied into image pro-cessing and event related potential extraction in recent years.Studies have shown that the coefficients of wavelet trans-form have more super-Gaussian nature in the probabilitydensity function and larger kurtosis than the original sig-nal. So ICA in wavelet domain has many significant ad-vantages, such as faster convergence speed of iteration andbetter performance of noise immunity. In this paper, theICA algorithm with discrete wavelet transform, denotedas DWICA, was investigated to remove ocular artifact, andthe criterion of angle cosine was introduced to recognizeocular artifact automatically and quickly.

The DWICA algorithm for ocular artifact suppressionin EEG is given as follows:

(1)The Mallat’s pyramid decomposition algorithm wasapplied to n channels of collected signals:

x(t) = [x1(t),x2(t), · · · ,xn(t)]T ,

suppose that the l-th column vector xl(t) is the referenceEOG and others are EEG signals. Then each channel xi(t)∈RM1×1(i = 1,2, · · · ,n) was decomposed by L-level decom-position tree, and the approximate coefficients and detailcoefficients were ranked in order to construct wavelet co-efficient vector xi(t) ∈ RM2×1, that is:

xi(t) = [Ai,L,Di,L,Di,L−1, · · · ,Di,1]T , i = 1,2, · · · ,n.

(10)Where M1 is the sample point of collected original signals,and M2 is the sample point of wavelet coefficient vectorxi(t).

(2)All coefficient vectors were combined to consideras the input of ICA algorithm, i.e.

x(t) = [x1(t), x2(t), · · · , xn(t)]T .

Then the FastICA algorithm based on negentropy criterionwas applied to estimate the separation matrix W , and the nchannels of independent components:

y(t) = [y1(t),y2(t), · · · ,yn(t)]T ,

were acquired quickly in wavelet domain by y(t) =Wx(t).(3)The criterion of angle cosine was applied to recog-

nize ocular artifact. The cosine of angle between each in-dependent component yi(t)(i= 1,2, · · · ,n) and wavelet co-efficient vector xl(t) of the reference EOG was calculatedby Eq.(11).

cosθi =

M2

∑q=1

yiqxlq√√√√ M2

∑q=1

y2iq

M2

∑q=1

x2lq

. (11)

Where M2 is the sample point of each wavelet coefficientvector.

All the absolute values of angle cosine were sorted indecreasing order, and the independent component corre-sponding to the biggest one was considered as ocular arti-fact, and it should be set to zero. Therefore, the indepen-dent component vector after elimination of EOG artifact

was rewritten as:

yi(t) =

{0, i f |cosθi|= max

j=1,··· ,n(∣∣cosθ j

∣∣),yi(t), Others.

(12)

The new independent component vector:

y(t) = [y1(t), y2(t), · · · , yn(t)]T ,

were used with inverse transform of ICA to project backonto the scalp electrodes by Eq. (13):

u(t) =W−1y(t) ∈ Rn×M2 . (13)

The Mallat’s pyramid construction algorithm was ap-plied to each channel of ICA-corrected wavelet coefficientsu(t) to reconstruct the artifact free EEG data. Thereby oc-ular artifact in EEG signals was removed and the signal tonoise ratio was improved greatly.

4. Experimental Research

In section 4, a mathematical model about EEG and EOGwas built according to the bi-directionality contaminationbetween the EEG and EOG signal, and the EEG data withocular artifact was constructed for experiments. Then, theproposed DWICA was applied to remove ocular artifactand quantitative performance indexes were introduced toevaluate the denoising effect. Furthermore, DWICA wasapplied into the real contaminated EEG data provided byEEG research center of Colorado Purdue University to provethe correctness and effectiveness of DWICA in EEG pre-processing.

4.1. EEG data construction for experiment

The clean EEG data was from the “BCI Competition 200”contest database. The data was recorded from a normalsubject (female, 25) during a feedback session. The subjectsat in a relaxing chair with armrests. The task was to con-trol a feedback bar by means of imagery left or right handmovements. The motor imagery experiment consisted of140 trials, of which the left hand movements and the rightone were 70 trials respectively. All trials were conductedon the same day. As shown in Fig.2 , each trial was 9 sin length with several minutes break in between. The first2 s was quite, at t=2 s, an acoustic stimulus indicated thebeginning of the trial, and a cross ”+” was displayed for 1s; then at t=3 s, an arrow (left or right) was displayed ascue. At the same time the subject was asked to move a barinto the direction of the cue [12].

The recording was made using a G.tec amplifier andsome Ag/AgCl electrodes. Three bipolar EEG channelswere measured over C3, Cz and C4 according to the In-ternational 10-20 System. The signals were sampled with128Hz and filtered between 0.5 and 30Hz, as shown in

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Figure 2 Timing scheme of experiment.

Fig.3(a) . The vertical EOG data from Colorado PurdueUniversity was filtered between 0.1 and 100Hz.

For the bi-directionality interaction between the EEGand the EOG signal, there exist the following equations :

EEGrec(t) = EEGclean(t)+ k1 ×EOGclean(t), (14)

EOGrec(t) = EOGclean(t)+ k2 ×EEGclean(t). (15)

Where k1 represents the propagation factor from the EOGto the EEG signal, and k2 represents the propagation factorfrom the EEG to the EOG signal. EEGclean and EOGcleanare the clean EEG and EOG signal respectively. EEGrecand EOGrec are the recorded EEG and EOG signal respec-tively.

In this experiment, it was supposed that the propaga-tion factors of EEG from three channels (C3, Cz and C4)to EOG were 0.05, 0.1 and 0.15 respectively. Meanwhile,the propagation factors from the EOG to the EEG of threechannels (C3, Cz and C4) were all set to 0.2. Consideringother artifacts such as muscle activity, pulse, sweat etc, agaussian white noise with 5 dBw was thus added into eachchannel to imitate the common influence of other noises.The constructed EEGs of three channels were presentedgraphically in Fig.3(b) , and it was easy to find that eachchannel of EEG data was influenced by EOG inordinately.Besides, the added gaussian white noise could reflect im-munity of artifact removal method.

4.2. Results of OA removal with DWICA

The EEG data of 140 trials contaminated by ocular artifactwas processed with DWICA. The iteration algorithm wasFastICA based on the negentropy, where the iteration pre-cision was set to 0.0001 and the maximum iteration num-ber was 10000. A Sym 8 wavelet filter was chosen in DWTand a 3-level decomposition was performed. Experimentresults were shown in Fig.4 . Compared the clean EEGdata in Fig.3(a) with the contaminated EEG data by ocu-lar artifact in Fig.3(b) , it was obvious that the magnitudeof OA was much higher than that of neural signals. FromFig.4 we could find that OA was removed from EEG dataand neural signals were recovered quite well with little in-formation leakage.

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(a) Clean EEG signals from International Stan-dard Database.

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Figure 3 Clean EEG and the constructed EEG and EOG.

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Figure 4 OA removed EEG signals with DWICA.

More experiments were done to compare DWICA pro-posed this paper with WT [3] , PCA [4], and ICA [6] inOA removal. Quantitative evaluations about the denoisingeffect and the average time consumption were given.

(1)Comparison on denoising effect base on Mean Sq-uared Error (MSE) index

MSE performance index was adopted to quantify andassess the denoising effect. MSE was calculated as fol-

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814 Mingai Li, Yan Cui, Jinfu Yang: Automatic Removal of Ocular Artifact...

lows:

MSE = { 1N

N

∑n=1

[s(n)− c(n)]2}. (16)

Where s(n) was the clean EEG data over an electrode,while c(n) was the corrected EEG data , and N was thenumber of sampling point. A smaller value of MSE meansthat the corrected EEG is more close to real EEG.

Before ocular artifact was removed, the average MSEof raw EEG data of 140 trials was 92.5299 (µV )2 for C3,92.4589 (µV )2 for Cz and 92.4313 (µV )2 for C4. Afterocular artifact removal with DWICA, the average MSEwas 3.9296 (µV )2 for C3,4.2042 (µV )2 for Cz and 4.0716(µV )2 for C4. Then WT, PCA and ICA were also usedfor OA removal and the comparison of average MSE wasshown in Fig.5 . It illustrates that the denoising effect ofDWICA is obviously better than WT and PCA, and alsomore effective than ICA. Therefore the automatic DWICAproposed in this paper is proved to be effective and prefer-able.

(2)Comparison on time consumptionIn the above experiments of 140-trials EEG data, four

methods were used to remove ocular artifact in three chan-nels, and the average running time was shown in Fig.6. Note that the average time consumption was 0.5768 sfor WT and 0.649 s for ICA, while 0.0406 s for DWICAin the same computing environment. So DWICA has thebest time efficiency significantly. Meanwhile, as shown inFig.6 , the average computation time of PCA was almostthe same as DWICA, while the denoising effect of OA re-moval by PCA was much worse than by DWICA, as shownin Fig.5 . Therefore considered both the denoising effectand time consumption together, DWICA is powerful.

Besides, it was found in the experiment that when ICAalgorithm in time domain was applied to remove ocularartifact, the noise in EEG data not only disturbed the sep-aration effect of ICA, but also resulted in the increasing ofiteration and calculation. In the total 140 trials, there were7 trials failed in solving the separating matrix W , namelyit wasn’t obtained when the maximum iteration numberreached 10000. However, ICA decomposition in waveletdomain was powerful in noise immunity, so all the 140trails could obtain the separation matrix W . Thus a con-clusion is drawn that DWICA can reduce the iterationsin FastICA algorithm and has great noise immunity. It ispreferable in EEG preprocessing on-line.

4.3. Power spectrum estimation

The power spectrum based on Autoregressive (AR) para-metric model is the major part of modern spectral estima-tion. It is widely applied in speech signal analysis, datacompression and communication. AR model power spec-trum estimation can reflect the energy of signal in fre-quency domain, and it improves the resolution of spec-trum estimation. The EEG power spectrum distortions ofthe cerebral activity introduced by the ocular artifact were

(a) with

the contaminated EEG data by ocular artifact in

it was obvious that the magnitude of OA

was much higher than that of neural signals. From

we could find that OA was removed from

and neural signals were recovered quite

! "$ %

Fig. 4.3. OA removed EEG signals with DWICA

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Cz

C3

MSE/(uV)2

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Figure 5 Comparison of denoising effect with MSE.

consumption

Time/s

0.0406

0.649

0.0513

0.5768

DWICA

ICA

PCA

WT

4.3Power spectrum estimation

Figure 6 Comparison of time consumption.

studied in this section. To assess the results of ocular ar-tifact removal in EEG, AR power spectrum of clean EEG(0.5-30Hz), contaminated EEG by OA, and corrected EEGby DWICA were all considered, as shown in Fig.7 . Itshows that the distortions of OA are generally in low fre-quency band, and AR model power spectrum of the cor-rected EEG with OA removed by DWICA matches per-fectly with the clean EEG signals. So we can conclude thatenergy of artifact free EEG data is recovered very well. Itproves that the proposed DWICA is correct and effectivein OA removal.

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Figure 7 Power spectrum comparison of EEG.

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4.4. DWICA in real contaminated EEG

In this section, DWICA was employed to real corruptedEEG data collected by cerebral research center of Col-orado Purdue University. There were seven volunteers tak-ing part in the experiment. Six bipolar EEG signals weremeasured over 6-channels of C3, C4, P3, P4, O1 and O2according to the International 10-20 System. Recordingswere made with reference to electrically linked mastoidsA1 and A2. One channel of vertical EOG was recordedbetween the forehead above the left browline and the leftcheekbone. The signals were sampled with 250Hz for 10seconds, 2500 samples and recordings were performed witha bank of Grass 7P511 amplifiers whose bandpass analogfilters were set at 0.1 to 100 Hz.

The data in this experiment was from subject 1 (a uni-versity male teacher of forty-eight years old did arithmeticmultiplication homework), here we only chose 3-channelsEEG over C3, C4, P3 and synchronous EOG data, as shownin Fig.8(a) . For clean cerebral data were unknown in prac-tical collection, no quantification evaluation about DWICAin OA removal was given. While from the comparison be-tween Fig.8(a) and Fig.8(b) , it is obvious that OA isremoved perfectly from EEG, and it proves that DWICAis also effective and powerful in real contaminated EEGrecordings. It provides a novel preferable idea for prepro-cessing of EEG data.

5. Conclusion

EOG contamination to EEG data is a common and im-portant problem in brain computer interface, disease di-agnosis and brain research etc. In this paper, a novel re-duction of ocular artifact in EEG signals is investigatedbased on discrete wavelet transform and independent com-ponent analysis. The criterion of angle cosine is introducedto judge OA automatically, and some quantitative perfor-mance indexes including MSE, AR-model power spectrumand time consumption are used in simulated mixtures withthe bidirectional contamination between the EEG and theEOG signals. The comparisons between DWICA and otherapproaches, namely WT, ICA and PCA, are performed forthe reduction of OA in simulated mixtures in order to showwhich ocular reduction technique is the best. In fact, theexperiment results indicate that the proposed DWICA ispowerful in noise immunity and fast in convergence rate.It provides a novel idea for on-line preprocessing of EEGsignals. Also it has a positive effect to further research andapplications in cerebral activity.

Acknowledgements

The work was supported by the Science and TechnologyProject of Beijing Municipal Education Commission (No.

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(a) Real contaminated EEG and EOG record-ings.

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(b) Corrected EEG data with OA removed.

Figure 8 DWICA application in real contaminated EEG.

KM201110005005), the Natural Science Foundation of Bei-jing (No.7132021,No.4112011)and the Fundamental Re-search Foundation of Beijing University of Technology(No.X4002011201101). We would like to thank all the peo-ple who have given us helpful suggestions. We also thankDepartment of Medical Informatics, Institute for Biomedi-cal Engineering, University of Technology Graz and Elec-trical Engineering Department, Colorado Purdue Univer-sity for the database.The authors are grateful to the anonymous referee for acareful checking of the details and for helpful commentsthat improved this paper.

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Mingai Li was born in 1966in Henan province. She receivedher B.E and M.E. from DaqingPetroleum Institute, Heilongjiang,China, in 1987 and 1990 re-spectively, and the Ph.D. de-gree from Beijing Universityof Technology, Beijing, China,in 2006. Her current researchinterests mainly include brain

computer interface, intelligent control, pattern recognitionand implementation of autonomous learning control tech-nology for flexible two-wheeled upstanding robot.

Yan Cui was born in 1987in Jiangsu province. She receivedher B.E from Nanjing Instituteof Technology, Nanjing, China,in 2010. She is currently a M.E.student in Beijing Universityof Technology. Her research in-terests include brain computerinterface, neural network, sig-nal processing and pattern recog-nition.

Jinfu Yang received the B.E.and M.E. in Control Scienceand Engineering from HarbinInstitute of Technology (HIT),Harbin, Heilongjiang, China, in2000 and 2003 respectively, andthe Ph.D. degree in Pattern Recog-nition and Intelligent Systemfrom National Laboratory of Pat-tern Recognition (NLPR), Chi-nese Academy of Sciences (CAS),

Beijing, China, in 2006. He is an associate Professor withthe College of Electronic Information and Control Engi-neering, Beijing University of Technology, Beijing, China.His research interests include pattern recognition, machinelearning, robot vision, robot navigation.

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