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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, MAY 2017 1

Removal of Eye Blink Artifactsfrom EEG Signals using Sparsity

Sreeja. S. R, Rajiv Ranjan Sahay, Debasis Samanta, Pabitra Mitra

Abstract—Neural activities recorded using electroencephalog-raphy (EEG) are mostly contaminated with eye blink (EB)artifact. This results in undesired activation of brain computerinterface (BCI) systems. Hence, removal of EB artifact is animportant issue in EEG signal analysis. Of late, several ar-tifact removal methods have been reported in the literatureand they are based on independent component analysis (ICA),thresholding, wavelet transformation, etc. These methods arecomputationally expensive and result in information loss whichmakes them unsuitable for online BCI system development. Toaddress the above problems, we have investigated sparsity-basedEB artifact removal methods. Two sparsity-based techniquesnamely morphological component analysis (MCA) and K-SVDbased artifact removal method have been evaluated in our work.MCA based algorithm exploits the morphological characteristicsof EEG and EB using pre-defined Dirac and discrete cosinetransform (DCT) dictionaries. Next, in K-SVD based algorithman over-complete dictionary is learned from the EEG data itselfand is designed to model EB characteristics. To substantiatethe efficacy of the two algorithms, we have carried out ourexperiments with both synthetic and real EEG data. We observethat the K-SVD algorithm, which uses a learned dictionary,delivers superior performance for suppressing EB artifacts whencompared to MCA technique. Finally, the results of both thetechniques are compared with the recent state-of-the-art FORCemethod. We demonstrate that the proposed sparsity-based al-gorithms perform as equal to the state-of-the-art technique.It is shown that without using any computationally expensivealgorithms, only with the use of over-complete dictionariesthe proposed sparsity-based algorithms eliminate EB artifactsaccurately from the EEG signals.

Index Terms—Electroencephalography (EEG), brain computerinterface (BCI), artifact removal, dictionary learning, K-SVD,morphological component analysis (MCA).

I. INTRODUCTION

THE objective of brain-computer interface systems (BCIs)is to provide human beings with an alternative mode of

communication for directly conveying human intentions to acomputer without the use of peripheral nerves and muscles ofhuman body [1]. For people with severe physical disabilities,such as limb damage, brainstem stroke, amyotrophic lateralsclerosis (ALS) [2], [3], spinal cord injury [4] or otherneuromuscular diseases, BCI is found to be the best mode tocommunicate [5]. Among the available non-invasive devices,EEG is unique and most often used in BCI research, as it

Sreeja. S. R, Debasis Samanta and Pabitra Mitra are with the Department ofComputer Science and Engineering, Indian Institute of Technology, Kharag-pur, 701302 India e-mail: ([email protected]; [email protected];[email protected]).

Rajiv Ranjan Sahay is with the Department of Electrical Engineer-ing, Indian Institute of Technology, Kharagpur, 701302 India e-mail: ([email protected]).

provides high temporal resolution of the measured brain sig-nals, is less bulky and mobile, relatively convenient, affordableand safe for users [3]. EEG measures the synaptic activity asvoltage fluctuations along the brain scalp due to the flow ofelectrical signals between the neurons [5]. Traditionally, EEGhas been used for medical BCI applications, but in recentyears, there has been a great interest in non-medical BCIapplications such as device control, training and education,and gaming/entertainment [3], [6], [7].

However, raw EEG signals are often contaminated by non-cerebral signals such as ocular artifacts, muscle artifacts,cardiac artifacts, power line interference and electrode arti-facts [8]. Although, reliable solutions exist for dealing withmost EEG artifacts it is really hard for human subjects toavoid ocular artifacts. The levator muscle potential developedas a result of eye blink (EB) is 10 times larger in amplitudethan the EEG and is the most dominant over other artifacts.On an average, a human involuntarily blinks once in 5s andthe blinking activity lasts for 100-400 ms [9], [10]. Due tohigh magnitude of the EB signal and high resistance of theskull and scalp tissues, the EB artifact contaminates majorityof the electrode signals [11]. Hence, EB artifact removal isan important problem in EEG signal processing. It is crucialas it can affect the detection and extraction of EEG featureswhich leads to undesired activations of a BCI system besidescausing frustration in users.

Although, in the literature there are several algorithmsavailable to remove EB artifacts, they still have major lim-itations. Some techniques use extra elctrooculography (EOG)channels [12], which may not always be available. Otherblind source separation techniques such as principal compo-nent analysis (PCA) [13], independent component analysis(ICA) [14] require prior knowledge of artifacts to detectartifactual sources or contaminated components and they arecomputationally expensive. In thresholding-based ICA tech-nique [15], the threshold value needs to be decided a priori inorder to avoid any information loss.

To address the above problems, we need a method or repre-sentation which can efficiently and accurately separate EB andartifact-free EEG signal without any prior information from theuser. Recently, sparse representation of natural signals has re-ceived great attention and has been used in many applicationssuch as image denoising [16], image compression [17], speechsignal compression [18] and speech signal classification [19].According to compressive sensing theory, many natural signalscan be sparsely represented when expressed using a convenientbasis [20]. Subsequently, numerical optimization procedurescan be used to perfectly reconstruct the original signal from




the sparse representation [21]. However, these advantages ofsparse representation are rarely studied for the purpose ofartifact removal. Hence, in this work, we investigated twodifferent sparsity-based approaches to remove EB artifactsfrom EEG data.

In the first sparsity-based approach, we propose to usemorphological component analysis (MCA) technique, whichseparates the EEG signals into components that have differentmorphological characteristics. Each morphological componentis sparse in an over-complete dictionary constructed by merg-ing pre-defined DCT and Dirac basis. From the obtained mor-phological components, the component corresponding to EBis subtracted from the original EEG signals to yield artifact-free EEG. In the second proposed sparsity-based method, K-SVD algorithm is applied wherein a sparsifying over-completedictionary is learned from the observed EEG signals contami-nated with EBs. Then using sparse coefficients and the learneddictionary, EBs are estimated and subtracted from the originalrecorded EEG signal. To the best of our knowledge, no workhas been done to remove EB artifacts from EEG using sparsityand over-complete learned dictionaries.

The proposed sparsity-based methods are designed to re-move EB artifacts from the EEG signals accurately withoutthe use of additional EOG recordings. Besides, unlike otherexisting methods [22], no knowledge of artifacts and noselection of artifactual sources is required. To demonstratethe efficacy of our proposed methods we have conductedseveral experiments on both synthetic and real EEG data.The results have been evaluated using different statisticalperformance metrics. Our experiments confirm the capabilityof the two proposed sparsity-based techniques for separationof EB artifacts from the EEG signal. The results show that,out of the two proposed techniques, K-SVD based artifactremoval method outperforms the MCA approach. Finally, theefficacy of these sparsity-based methods has been evaluatedand compared with the recent state-of-the-art FORCe (FullyOnline and Automated Artifact Removal for Brain-ComputerInterfacing) method [15]. Note that we do not use independentcomponent analysis (ICA), wavelet decomposition and higherorder statistical properties of EEG signal. Only with a singleover-complete dictionary learned from the given EEG data,the proposed K-SVD based method eliminates EB artifactsaccurately from the observed EEG signal.

Our paper is organised as follows. In Section II, we presentan overview of related research works. In Section III, weprovide the mathematical background of proposed techniquesalong with algorithms for the purpose of EB artifact removal.The experimental results using both simulated and real EEGdata are presented in Section IV. Comparison of the proposedapproaches with the existing state-of-the-art techniques isgiven in Section V. In Section VI, a discussion regardingphase delay and computation time of the proposed methodsare given. Finally, conclusions and directions for future workare outlined in Section VII.

II. LITERATURE SURVEY

Investigating EB artifact properties is important for re-searchers to devise novel techniques for their removal. A

variety of methods have been proposed to remove ocularartifacts [12–15], [22–29]. One of the primary approachesto restrict EB is eye fixation on a stationary target [23]. Inthis method the user is asked to stare at a stable point forfixed amount of time which is unrealistic and not suitablefor BCI applications. Further, this fixation method [23] doesnot eliminate involuntary eye blinks [14]. Regression basedmethods [25] play an important role in the removal of ocularartifacts including blinks. Artifacts in EEG can be removed bysubtracting the weighted noise (EOG) from signals. However,the main disadvantage of this method is that it requires extraocular channels to record ocular artifacts [12].

There is also accord among researchers in using techniquessuch as principal component analysis (PCA) [30], independentcomponent analysis (ICA) [14], wavelet based denoising [24]for ocular artifact correction. Among these algorithms, ICAis a well-known blind source separation (BSS) technique toremove ocular artifacts from EEG signals. ICA based methodsassume that the EEG recordings can be considered to be alinear mixture of several components, namely, non-artifactualand artifactual independent components (ICs). Artifactual ICsrepresent potential of non-cerebral artifacts whereas non-artifactual ICs represent electrical signals originating frombrain. After detecting the ocular artifact components andeliminating them manually, the clean EEG can be acquiredby remixing remaining components [26]. The most importantissue in ICA based artifact removal methods is selecting thecorrect number of ICs, that is, extracted components whichshould be deflated [27]. Since these ICs do not unavoidablyonly contain artifact data, but also contain the fundamentalEEG data, removing contaminated ICs will lead to loss of use-ful data [28]. Hence, some researchers incorporated empiricalmode decomposition (EMD) [29], wavelet decomposition [12],wavelet neural network [22] and higher order statistics suchas multiscale sample entropy [10] and kurtosis [15] intotheir ICA-based algorithms to extract specific sources withknown behaviours. However, many of these approaches are notsuitable for BCI applications since they are computationallyexpensive.

In recent years, algorithms exploiting sparse representationof signals have witnessed a remarkable growth in variety ofapplications such as image compression [17], speech signalcompression [18] and image denoising [16], but they havebeen rarely studied for the purpose of artifact removal fromEEG signals. For instance, in [31] and [32], pre-defined basisare applied to sparsely represent EEG signals for removingexisting artifacts. The work in [33] successfully removesballistocardiogram (BCG) artifact modeled from EEG-fMRIdata taking advantage of sparse representation and learneddictionaries. MCA technique finds use in image-content sep-aration problem [34] where it is used to separate the textureand the cartoon parts. It has also been applied to signal sep-aration, image separation, biomedical engineering and speechprocessing [35]. The success of MCA is dependent uponthe choice of appropriate dictionaries (basis functions). Inthe literature, curvelet dictionaries are found appropriate torepresent piecewise smooth images [36], wavelet dictionariescan be used to represent isotropic structures [37] and ridgelet




dictionaries are highly recommended to represent global linesin an image [38]. If an appropriate dictionary is identified,the use of sparse representation will lead to the desired signalseparation [35]. In K-SVD based artifact removal method in-stead of using pre-chosen set of basis functions, it is proposedto learn the over-complete dictionary from examples [16]. K-SVD algorithm is an iterative method that updates sparsecoefficients and dictionary atoms jointly, which results inaccelerated convergence [39]. K-SVD algorithm is provento be simple, flexible and efficient for the image denoisingproblem [16].

III. METHODOLOGY

In this section, each of the two proposed sparsity-basedmethodologies is described briefly and their mathematicalbackground is given.

A. Proposed EB removal techniques

The framework of the proposed approach is given in Fig 1.The first task in sparsity-based approach is to obtain thel0-norm sparse representation for the observed EEG signal.With the obtained sparse solution, the degraded EEG signalsare modeled using MCA and K-SVD approaches. In MCAmethod the dictionaries are pre-defined and remain unchangedthroughout the process of estimation of sparse coefficients. InK-SVD method the randomly initialized dictionary is updatedusing the estimated sparse solution. Then with the estimatedsparse solution and the updated dictionary matrix, EB artifactsare estimated and subtracted from the degraded EEG signal toobtain clean EEG data. The proposed algorithms are explainedbriefly in the following sections.

1) Sparse representation of signals: The objective of sparserepresentation is to compute the sparse coefficients sss ∈ Rk,given the observed signal yyy ∈ Rm and over-complete dic-tionary matrix D ∈ Rm×k (where k > m implies redun-dancy) [16]. The EEG signal yyy of length m is divided into nsegments yyy = {yyyi}

ni=1. Each of n segments {yyyi}

ni=1 are rep-

resented by {sssi}ni=1 sparse coefficient vectors. The dictionaryD can be randomly generated or produced from the collectionof several known basis functions such as wavelet, curvelet,Fourier, DCT, Dirac bases [40]. The columns of the dictionarymatrix D are called atoms and denoted as dddj , j = 1, 2.., k.Assuming D as fixed, the signal segment yyyi can be representedas a sparse linear combination of atoms of dictionary {dddj}kj=1,such that yyyi = Dsssi with good approximation. Infinite numberof solutions are possible if D is a full-rank matrix; hence priorconstraints must be imposed on the solution [39], i.e.

minsssi‖sssi‖0 subject to yyyi = Dsssi (1)

where ‖.‖0 is l0- norm, counting non-zero entries in sssi.Solving equation (1) is both numerically unstable and NP-hard,requiring enumeration of all possible locations of nonzeroentries in si [21]. Nevertheless, methods exist in the literaturefor obtaining an approximate solution which is found to bequite accurate if the solution is sparse enough [41], [42]. In thiswork, orthogonal matching pursuit (OMP) algorithm has been

used to find an approximate solution of equation (1) becauseof its efficiency and simplicity [43].

Algorithm 1: OMP algorithm: sss = OMP (yyy, D, T )Input: Observed signal yyy, Dictionary D, sparsity

threshold TOutput: sssInitialize: the residual rrr0 = yyy, the index set I0 = ∅, t = 1while t < T do

it = arg maxj=1,2,..,k |〈rrrt−1, dddj〉|It = It−1 ∪ {it}ssst = ddd†Ityyyrrrt = yyy −Dssstt = t+ 1

endreturn sss = ssst

The steps of OMP [41] which is a greedy technique havebeen outlined in Algorithm 1. At each iteration, this algorithmattempts to find the atoms, that are maximally correlated withthe residual vector rrr. This implies that the selected atomhas maximum information and hence maximally minimizesthe error in reconstruction. Then OMP appends the indexof selected atoms it to the active set It. In the next step,orthogonalization is carried out, where ddd†It indicates Moore-Penrose pseudo-inverse of dddIt i.e., ddd†It := (dddTItdddIt)

−1dddTIt .More importantly, this algorithm ensures that the residual rrrtis always orthogonal to atoms indexed in It. Accordingly,the correlation of the active atoms It will be zero in thenext iteration. Hence, no atom is selected twice in OMP. Theiteration is repeated until the pre-defined threshold T whichcontrols the sparsity is attained.

2) Morphological Component Analysis (MCA): This is amethod for decomposing a signal into its parts posessingdifferent morphological characteristics [31], [32]. MCA de-pends on the hypothesis that for every signal to be separated,there is a dictionary of atoms that allows its reconstructionusing a sparse representation [35]. With the benefit of sparserepresentation sss, it assumes that a signal yyy can be representedas linear combination of N morphological components, yyyi:

yyy = ΦsΦsΦs =

N∑i=1

ψisi =

N∑i=1

yyyi (2)

where ΦΦΦ = {ψ1, ψ2, ...ψN}. Note that {ψ1, ψ2, ...ψN} are thebasis matrices or dictionaries and sss = {s1, s2, ..sN} are thecorresponding sparse coefficient vectors. Choosing the correctdictionary is crucial to create a good sparse decomposition.If appropriate dictionaries are used, then the OMP algorithmsearching for the sparsest solution can lead to the desiredseparation of EEG and EB artifacts. For the problem at hand,discrete cosine transform dictionaries are used to represent thebackground EEG signals [32] and Dirac dictionaries have beenused to represent EB artifacts [44].

In this work, the degraded EEG signal yyy is considered as alinear combination of two morphological components yyy1, yyy2obtained using DCT and Dirac basis and it is represented as

yyy = yyy1 + yyy2 = ψDCT sDCT + ψDsD (3)




Observed EEG signal with EB

artifacts

Estimate sparse representation of

EEG signalK-SVD method

Update / Learn the given dictionary

MCA methodPre-defined basis functions are used Estimate EB using

sparse solution and dictionary

Clean EEG = Observed EEG –

Estimated EBOR

Fig. 1: Framework of the proposed approach.

where sDCT and sD are the sparse coefficient vectors cor-responding to the DCT and Dirac basis, ψDCT and ψD,respectively. The steps followed to separate EEG and EBcomponents using MCA technique are given in Algorithm 2.Using the estimated morphological components, the compo-nents corresponding to the EB artifact are subtracted from theoriginal degraded EEG to obtain the artifact-free signal.

Algorithm 2: MCA algorithmInput: Observed signal yyy, dictionaries ψ1, ψ2, ..ψN ,

number of iterations Niter, sparsity threshold TOutput: Morphological components yyy1, yyy2, ..yyyNInitialize: r0n = yyy, yyy0n = 0for t = 1...Niter do

for n = 1...N doupdate residual rtn = rt−1n

compute sparse coefficient vector, stn, using OMPstn = OMP (rtn, ψn, T )update nth component yyytn = ψns

tn

compute residual rtn = yyy −∑Nn=1 yyy

tn

endend

3) K-SVD based artifact removal method: In the previoussection, to find sparse vector sss, the dictionary (D = ΦΦΦ) is pre-defined and assumed fixed. However, such a matrix may not bethe optimal over-complete dictionary. Hence, in this work K-SVD algorithm is used to fine-tune or update the dictionary D,one atom at a time, so as to further reduce the reconstructionerror. This algorithm is flexible and works with any pursuittechnique. Mathematically, the dictionary learning problem forthe given signal yyy = {yyyi}

ni=1 can be defined as

minD,sssi

n∑i

‖yyyi −Dsssi‖2F subject to ‖sssi‖0 ≤ T (4)

where ‖.‖F denotes Frobenius norm. The parameter T controlsthe number of non-zero entries in sssi and should satisfy T �k, where k is the number of atoms in the dictionary D i.e.,D = {dddj}kj=1. K-SVD algorithm consists of two steps. In thefirst step it finds the sparse representation vector sssi using OMPalgorithm. Subsequently, in the second step the dictionary Dis updated such that it best represents the observed signal yyyifor the estimated coefficient vector sssi. For a detailed study ofK-SVD algorithm we refer the reader to [39]. This algorithmis called “K-SVD” because it carries out K number of singularvalue decomposition (SVD) computations to obtain the learneddictionary D [39]. Here, K = k, the number of atoms inthe dictionary, and each atom in D is updated as outlined inAlgorithm 3.

Algorithm 3: K-SVD algorithmInput: Observed signal yyy, initial estimate of dictionary

D = D0, sparsity parameter T , K denotesnumber of atoms in dictionary, N is the numberof K-SVD iterations.

Output: D,sssfor t = 1...N do∀i si = OMP (yyyi, D, T )for l = 1...K do

Find the set of signals that use the current atom,wl =

{i | 1 ≤ i ≤ n, sl 6= 0

}Compute overall representation error ElEl = yyy −

∑j 6=l dddjsss

j

Set El as matrix by choosing only those columnsin wl and obtain ERl

[U, S, V T ] = SV D(ERl )Update the dictionary column dddl with the firstcolumn of Usssl ⇐ S1,1 ∗ V1

endend

We denote the given raw EEG signal by yyy, the clean EEGsignal as ccc and EB artifact as zzz, then these quantities can berelated as

yyy = zzz + ccc (5)

The aim of this work is to model the eye blink zzz using dictio-nary learning technique so as to separate it from yyy. Initially,this method is applied to single channel EEG data yyy of lengthm. For learning the dictionary, we require n training signalsas input. Hence the original signal is divided into n smallersegments which can have varying degrees of overlap with eachother. In this work, we used fully overlapping segments inorder to capture the behaviour of the entire observed EEGsignal. However, our main objective is to remove EB artifactfrom EEG, hence the original dictionary learning problem ismodified as:

minD,si,z

λ ‖yyy − zzz‖22 +∑i

µi ‖sssi‖0 +∑i

‖Dsssi −Rizzz‖22 (6)

Here, the first term is the Euclidean norm between observedEEG yyy and eye blink zzz and λ is a Lagrange multiplier.The other terms are equivalent to the problem of dictionarylearning. Ri is a binary matrix which extracts ith segmentfrom zzz i.e. zzzi = Rizzz. The parameter µi controls the levelof sparsity. To calculate zzz we assume D and sssi as fixed in




equation (6) and take its derivative with respect to zzz equatingit to zero. The solution is given as

zzz =

(λI +

∑i

RTi Ri

)−1(λyyy +

∑i

RTi Dsssi

)(7)

In the above equation, I refers to an identity matrix, zzz is theestimated EB artifact of the observed EEG signal. Subtractingit from yyy gives us the clean EEG ccc i.e. ccc = yyy− zzz. In equation(7) zzz can be calculated sample-wise so that handling hugematrices can be avoided.

B. Performance metrics

The performance of our proposed artifact removal methodsis evaluated using various quantitative metrics such as rootmean square error, signal to artifact ratio, correlation coeffi-cient, mutual information and power spectral density.

Root Mean Square Error (RMSE) or Root Mean SquareDeviation (RMSD) [22] is the measure of difference betweenthe estimated artifact-free EEG signal (ccc) and the data actuallyobserved (yyy). This difference is also called as residuals andRMSE aggregates them into a measure of predictive power. Itis computed using the equation:

RMSE =

√∑nt=1

(yyyt − ccct

)2n

(8)

where n is the length of the corrupted EEG signal yyy.Signal to Artifact Ratio (SAR) is a method to measure the

amount of artifact removed from the observed EEG signalafter processing with the proposed methodologies [45]. SARbetween the observed EEG signal with artifacts (yyy) and theartifact-free signal (ccc) is given as

SAR = 10 log10std(yyy)

std(yyy − ccc)(9)

where std refers to standard deviation. Higher value of SARindex indicates superior performance of the algorithm.

Correlation Coefficient (CC) measures the degree of asso-ciation between the degraded original EEG with EB artifacts(yyy) and the artifact-free EEG (ccc). More positive the correlationvalue, stronger is the correlation [46]. Suppose C(yyy, ccc) is thecovariance, then CC of yyy and ccc is given as:

CC =C(yyy, ccc)√

C(yyy,yyy) ∗ C(ccc, ccc)(10)

Mutual information (MI) measures how much relevant in-formation the degraded original EEG with EB artifacts (yyy)shares with the artifact-free EEG (ccc) [47]. Mathematically, itis defined as

MI =

∫ ∞−∞

∫ ∞−∞

p(yyy, ccc)log

(p(yyy, ccc)

p(yyy)p(ccc)

)dyyydccc (11)

where p(yyy, ccc) is the joint probability density function and p(yyy)and p(ccc) are the marginal probability density functions of yyyand ccc, respectively. The resemblance between the observedEEG signal and the artifact-free EEG signal is more if themagnitude of MI is large [10].

To estimate the similarity in frequency content betweenthe raw EEG signal (yyy) and the artifact-free EEG signal(ccc), magnitude squared coherence (MSC) [10] measure iscomputed. Mathematically, it is defined as follows:

MSCyyy,ccc (f) =PSDyyy,ccc (f)

2

PSDyyy,yyy (f)PSDccc,ccc (f)(12)

where PSDyyy,ccc denote the cross-spectral density between yyyand ccc, PSDyyy,yyy and PSDccc,ccc denote the auto-spectral densityof yyy and ccc, respectively.

IV. EXPERIMENTS AND RESULTS

We used both synthetic and real EEG data in our exper-iments. The synthetic data was used to evaluate the perfor-mance of the proposed approaches whereas the real EEGdataset was used to validate them.

A. Experiments with Synthetic EEG Data

The design of synthetic EEG data consists of two parts:creation of EEG-like signal and EB-like artifact. Consideringthe noise-like nature of EEG signal we decided to choose 1/for pink noise to simulate our EEG signal. The EB-like artifactsignal is created using several combinations of sinusoids withdifferent amplitudes and frequencies. The created EEG-likesignal ccc, EB-like signal zzz and their summation as noisy EEGsignal yyy are shown in Figs 2 (a), (b) and (c), respectively. Asin a real-world EEG signal, pink noise which resembles EEGalso has lower amplitude than EB signal.

0 1 2 3 4 5 6 7 8 9Time (seconds)

(a)

-0.50

0.51

1.5

Am

plitu

de (µ

V)

Generated EEG-Pink Noise (c)

0 1 2 3 4 5 6 7 8 9Time (seconds)

(b)

-0.50

0.51

1.5

Am

plitu

de (µ

V)

Generated artifact-Eye Blink (z)

0 1 2 3 4 5 6 7 8 9Time (seconds)

(c)

-0.50

0.51

1.5

Am

plitu

de (µ

V)

Noisy EEG signal (y)

Fig. 2: (a) Simulated EEG-like signal, ccc; (b) EB-like signal, zzz; (c) theirsummation, yyy.

Initially, we use the MCA technique to obtain the morpho-logical components from the synthetically generated corruptedEEG signal of Fig. 2 (c). DCT and Dirac basis are selectedas the two dictionaries in the MCA algorithm because thenoise-like low amplitude EEG-like signal can be representedby DCT basis [32] and the spike-like components caused byEB-like signal can be sparsely represented by Dirac basis [44].The MCA algorithm decomposes the generated noisy EEGsignal into EB artifact and EEG signal as shown in Figs. 3(b) and (c), respectively. The extracted EB artifact in Fig. 3(b) is similar to the generated EB artifact in Fig. 2 (b).The correlation coefficient between the extracted EB artifactand the generated EB artifact is 0.7832. As our aim is toextract artifact-free EEG, the quantitative metrics are measured




between the generated pink noise in Fig. 2 (a) and extractedEEG signal in Fig. 2 (c). The quantitative metrics such asRMSE, CC, SAR and MI values listed in Table I show goodperformance of MCA algorithm.

0 1 2 3 4 5 6 7 8 9Time (seconds)

(a)

-1

0

1

Am

plitu

de (µ

V)

Noisy EEG Signal

0 1 2 3 4 5 6 7 8 9Time (seconds)

(b)

-1

0

1

Am

plitu

de (µ

V)

Extracted EB artifact

0 1 2 3 4 5 6 7 8 9Time (seconds)

(c)

-0.5

0

0.5

Am

plitu

de (µ

V)

Extracted EEG signal

Fig. 3: (a) Generated noisy EEG signal; (b) Extracted EB-like signal; (c)Artifact-free EEG-like signal estimated using MCA method.

Next, the K-SVD based dictionary learning artifact removalmethod is applied over the noisy EEG signal yyy of Fig. 2 (c).The signal yyy of length m is divided into n smaller segments.For example, if a random dictionary of size 100 × 150 ischosen, then the signal of length 1200 samples is divided inton segments where each segment includes N (=100) sampleswhich depends on the size of the dictionary. That is, if thedictionary size is p×q, then the number of samples N in eachsegment is equal to p. The number of segments n, depends onthe degree of overlapping. Considering again the EEG signal,yyy, of length m = 1200 samples, if there is no overlap betweensegments then the value of n is 12. For partial overlap with50% coverage n is 23 and for fully overlapping segments nis 1101. In this work we consider fully overlapping segmentsand hence the K-SVD technique outlined in Algorithm 3 isapplied to 100 samples of each segment to obtain the sparsecoefficient vector of size 150× 1 with 5 non-zero coefficientsand the learned dictionary D ∈ R100×150.

This process is repeated for each segment with full overlapuntil the entire signal is covered. Then the obtained sparsecoefficient vectors and the learned dictionary are used inequation (7) to separate EB-like signal and EEG-like signalwhich are shown in Figs. 4 (b) and (c), respectively. Theextracted EB artifact using K-SVD technique is very similarto the generated EB artifact in Fig. 2 (b) and the correlationcoefficient between them is 0.8214. The quantitative metricsmeasured between the generated EEG signal in Fig. 2 (a) andthe extracted EEG like signal in Fig. 4 (c) are listed in Table I.From the values in the Table I it is observed that K-SVD basedartifact removal method outperforms MCA method since itproduces smaller RMSE value and higher CC, SAR and MIvalues. The magnitude squared coherence (MSC) between thegenerated noisy EEG signal yyy and the extracted noise-freeEEG signal by MCA and K-SVD method are shown in Figs. 5(a) and (b), respectively. It clearly shows that both the sparsity-based methods have strongest coherence above 10 Hz andtherefore, preserve the neural signal frequency after removingthe EB-like artifacts.

0 1 2 3 4 5 6 7 8 9Time (seconds)

(a)

-1

0

1

Am

plitu

de (µ

V)

Noisy EEG Signal

0 1 2 3 4 5 6 7 8 9Time (seconds)

(b)

-0.5

0

0.5

1

Am

plitu

de (µ

V)

Extracted EB artifact

0 1 2 3 4 5 6 7 8 9Time (seconds)

(c)

-0.5

0

0.5

Am

plitu

de (µ

V)

Extracted EEG signal

Fig. 4: (a) Generated noisy EEG signal; (b) Extracted EB-like signal; (c)Artifact-free EEG-like signal estimated uisng K-SVD method.

0 10 20 30 40 50 60Frequency [Hz]

(a)

0

0.5

1

Coh

eren

ce0 10 20 30 40 50 60

Frequency [Hz]

(b)

0

0.5

1

Coh

eren

ce

Fig. 5: MSC of the generated noisy EEG signal yyy and the extracted artifact-free EEG-like signal using (a) MCA method and (b) K-SVD method.

TABLE I: Comparison of RMSE, CC, SAR and MI values of K-SVD andMCA methods computed using simulated EEG (pink noise) like data andextracted EEG like data.

Metrics K-SVD MCA

Root Mean Square Error (RMSE) 4.6731 6.7164Correlation Coefficient (CC) 0.7892 0.6831

Signal to Artifact Ratio (SAR) 3.4592 1.7722Mutual Information (MI) 0.7241 0.6468

B. Experiments with Real EEG data

1) Experimental Setup: Seven healthy subjects (four malesand three female), between ages of 23 and 30, volunteeredfor the experiment. Emotiv EPOC+ [48] device with samplingrate of 128 samples per second, with low pass filter cut-off frequency 45 Hz and resolution of 14 bits was used torecord data. Users were seated in a chair with their arms andlegs extended, resting on a desk and resting on a footrest,respectively. We made sure that there was no background noisewhile the data was recorded. Initially, the user is asked to closehis/her eyes for 10 seconds. Then they were asked to openand sit relaxed for one minute and again instructed to closetheir eyes for 10 seconds. We could see that most of the usersblinked their eyes once in 5 seconds or at least eight times in aminute. So, EEG data of total one minute 20 second duration iscollected in each trial for each user. The experiment is repeatedtwo times with all users. Hence, a total of 14 datasets werecollected and used. Each session lasted about 10 - 15 minutes,including the time for experimental setup and data recording.The data was captured on all 14 channels which were labelledas AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8,




AF4 and channels P3, P4 were used as reference channels. Therecorded EEG signals are transferred to the computer usingwireless USB connector.

2) Experiments with Single channel EEG data:Case 1 (Subject S1 - dataset 1, S1 1): Out of 14 channels

acquired using Emotiv EPOC+ device, the data of AF3 channelis severely affected by EB artifacts as it is located overthe dorsolateral prefrontal cortex [10]. Fig. 6 (a) shows theacquired AF3 EEG signal with EBs for the subject S1 1.Before using the proposed artifact removal techniques, wenormalized the data by subtracting the mean value of thesignal from each data point. Then the proposed MCA and K-SVD based EB artifact suppression techniques were appliedto AF3 channel data. MCA method decomposes the acquiredEEG signal in Fig. 6 (a) into EB component and artifact-freeEEG component. The artifact-free EEG obtained from MCAmethod is shown in Fig. 6 (b). The result shows that MCAmethod removes EB artifacts from the AF3 channel data toa significant degree. However, one can observe that some EBartifatcs still exist in the estimated artifact-free EEG signal.

0 4 8 12 16 20 23

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Fig. 6: (a) Acquired AF3 channel data with EB for subject S1 1; (b) Artifact-free EEG estimated using MCA method; (c) Artifact-free EEG estimated usingK-SVD method

Similar to experiments on synthetic data, the normalisedsignal of length m samples is divided into n segments. Eachsegment of N (=100) samples is processed using Algorithm 3to obtain the sparse coefficients and the learned dictionary. Asexplained previously in section IV A, the choice of value of Ndepends on size of the dictionary. Finally, we use equation (7)to separate EB contribution from the AF3 channel data. Theartifact-free EEG obtained from K-SVD technique is shown inFig. 6 (c). The result shows that K-SVD method successfullyremoves EB artifacts from single channel EEG data.

By comparing the results of applying the proposed methodson the EEG signal corrupted with EB artifacts, we observe thatthe K-SVD method removes all the EB artifacts and yields anartifact-free EEG signal. Hence, the learned dictionary of K-SVD algorithm delivers superior performance for removingartifacts than using pre-defined DCT and Dirac dictionariesin the MCA technique. We reach the same conclusion byevaluating quantitative metrics such as RMSE, CC, SAR andMI for both the sparsity-based approaches. The metrics arecalculated between the acquired raw AF3 channel signal andthe artifact-free signal obtained by applying the proposed

sparsity-based approaches. The values obtained are listed inTable II. We observe from Table II that the proposed K-SVDbased artifact removal method outperforms MCA method sinceit produces smaller RMSE value and higher CC, SAR and MIvalues. The MSC plots obtained for the acquired AF3 channeldata with EBs of subject S1 1, and the estimated artifact-freeEEG signal using MCA and K-SVD method are shown inFigs. 7 (a) and (b), respectively. The coherence values showthat the sparsity-based artifact removal algorithms preserve theneural signal frequency after removing the EB artifacts.

TABLE II: Comparison of RMSE, CC, SAR and MI values of K-SVD andMCA methods for single channel data for the subject S1 1.

Metrics K-SVD MCA



0 10 20 30 40 50 60Frequency [Hz]

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Fig. 7: MSC of the acquired AF3 channel data for the subject S1 1 andthe extracted artifact-free EEG signal using (a) MCA method and (b) K-SVDmethod.

Case 2 (Subject S3 - dataset 2, S3 2): Out of seven subjects,subject S3 involuntarily blinked two times at certain instancesduring experimental trials. We found that this was a mannerismof the particular subject. We observed that our proposedsparsity-based methods remove such blinks accurately fromthe EEG signals. The acquired AF3 channel data for thesubject S3 2 with double EBs is shown in Fig. 8 (a) andthe observed double EB artifacts are shown encircled with anellipse. Artifact-free EEG signals obtained using MCA and K-SVD methods for the subject S3 2 are shown in Figs. 8 (b)and 8 (c), respectively. The quantitative metrics obtained arelisted in Table III and it is observed that K-SVD based artifactremoval method outperforms MCA method in removing EBartifacts. The MSC obtained for the acquired AF3 channeldata with double EBs for the subject S3 2 and the extractedartifact-free EEG signal using MCA and K-SVD method isshown in Figs. 9 (a) and (b), respectively.

3) Experiment with 14-channel EEG data (Subject S4 -dataset 1, S4 1): Next, we use both MCA and K-SVDbased artifact removal methods on all the 14 channel EEGdata acquired using Emotiv Epoc+ device. Fig. 10 shows theobserved EEG signals of all the 14 channels with EB artifactsfor the subject S4 1. First, MCA technique is applied to all the14 channels by keeping the two pre-defined DCT and Dirac




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Fig. 8: (a) Acquired AF3 channel data with double EBs for the subject S3 2;(b) Artifact-free EEG estimated using MCA method; (c) Artifact-free EEGestimated using K-SVD method

TABLE III: Comparison of RMSE, CC, SAR and MI values of K-SVD andMCA methods for single channel data for the subject S3 2.

Metrics K-SVD MCA



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Fig. 9: MSC of the acquired AF3 channel data with double EBs for the subjectS3 2 and the extracted artifact-free EEG signal using (a) MCA method and(b) K-SVD method.

dictionaries unchanged throughout the process. To attain thistask, the extracted signals from all the channels are dividedinto segments of size N samples, and each segment is thenused as input to Algorithm 2. The MCA algorithm decomposesthe acquired EEG signal into EB artifact and artifact-freeEEG signal. We follow a similar procedure for processing 14-channel EEG data using K-SVD method. The only differencehere is that instead of pre-defined dictionaries, a single over-complete dictionary is learned. Algorithm 3 and equation (7)are applied to each segment of raw EEG signal to learn a singleover-complete dictionary. Fig. 11 shows the artifact-free EEGobtained by applying the proposed K-SVD based technique onthe acquired EEG data of all the 14 channels for the subjectS4 1 shown in Fig. 10.

The performance of the proposed sparsity-based techniqueson all the fourteen datasets is evaluated using metrics such asRMSE, CC, SAR and MI. Each dataset consists of 14 channelEEG data. The performance metrics are applied separatelyto each channel data of each dataset. The averaged RMSE,CC, SAR and MI values of each channel for all the fourteen

0 5 10 15Time (seconds)

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Fig. 11: Artifact-free EEG signals using K-SVD method for the subject S4 1.

datasets is enumerated in Table IV. The total average value ofRMSE (7.6801, 8.5167), CC (0.6738, 0.5561), SAR (2.0282,1.3606) and MI (1.0102, 0.8795) demonstrate that K-SVDbased artifact removal method outperforms MCA method. Theaverage MSC obtained for all the 14 channels for one of thesubjects (S4 1) is shown in Figs. 12 (a) and (b), respectively.

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Fig. 12: Average MSC of all the 14 channel data for the subject S4 1 andthe extracted artifact-free EEG signal using (a) MCA method and (b) K-SVDmethod.

V. COMPARISON WITH EXISTING WORK

To evaluate the performance of the proposed K-SVD-basedartifact removal method, we provide a comparison of statisticalmetrics with a very recently proposed artifact removal methodnamely, Fully Online and Automated Artifact Removal forBrain-Computer Interfacing (FORCe) method [15]. FORCetechnique is based upon a combination of wavelet decom-position, ICA, hard and soft thresholding. FORCe is mainlyintended for the removal of participant generated artifactsand it has been shown to outperform state-of-the-art artifact




TABLE IV: Comparison of average RMSE, CC, SAR and MI values ofK-SVD and MCA based EB artifact removal methods for all the 14 channelEEG data of all the fourteen datasets.

Metrics Channel location K-SVD MCA

Average RMSE AF3 12.0352 13.4814F7 9.5141 10.6113F3 7.4345 8.1279

FC5 11.8764 12.4589T7 5.5465 6.0214P7 5.2163 5.5242O1 5.3690 5.6348O2 5.8525 6.0259P8 6.3476 6.9884T8 7.3451 8.2486

FC6 4.3430 5.5473F4 8.4465 8.9759F8 6.5932 8.6424

AF4 11.6016 12.9456Total average RMSE 7.6801 8.5167

Average CC AF3 0.5048 0.4124F7 0.6812 0.5182F3 0.6488 0.5876

FC5 0.7028 0.4849T7 0.7998 0.6731P7 0.7368 0.6444O1 0.7285 0.6744O2 0.7960 0.6417P8 0.6679 0.5037T8 0.6843 0.6284

FC6 0.7025 0.5638F4 0.6215 0.4515F8 0.6299 0.5184

AF4 0.5191 0.4837Total average CC 0.6738 0.5562

Average SAR AF3 1.4129 0.7378F7 1.6427 0.8721F3 2.1261 0.9666

FC5 1.3122 0.5107T7 3.1374 1.9571P7 2.6438 1.5914O1 1.5953 1.5356O2 2.9674 1.8752P8 1.3994 1.1735T8 3.5603 2.8642

FC6 3.2868 2.3035F4 1.2353 0.5461F8 1.4598 0.9890

AF4 0.6150 0.3657Total average SAR 2.0282 1.3063

Average MI AF3 0.6963 0.6585F7 0.7939 0.7489F3 1.1629 1.0580

FC5 1.1696 1.0468T7 1.3416 0.9841P7 1.1728 1.0603O1 1.0723 0.9419O2 1.2517 1.0423P8 1.1590 0.8482T8 0.9141 0.8705

FC6 1.0438 0.9619F4 1.0576 0.8453F8 0.6588 0.6157

AF4 0.6476 0.6298Total average MI 1.0102 0.8795

Fig. 13: Average CC, SAR, RMSE and MI values obtained by K-SVD, MCA and FORCe methods for all the 14 channels of all thefourteen datasets.

removal methods such as Lagged Auto-manual InformationClustering (LAMIC) [49] and Fully Automated StatisticalThresholding for EEG artifact Rejection (FASTER) [50].

The average RMSE, CC, SAR and MI values of ourproposed K-SVD method and the existing FORCe method forall the fourteen datasets are enumerated in Table V and theaverage values of each channel for all the fourteen datasetis shown in Fig. 13. From Table V it is shown that theRMSE (7.6801, 7.6702), CC (0.6738, 0.6853), SAR (2.0282,2.0565) and MI (1.0102, 1.0154) values of K-SVD algorithmand FORCe method, respectively are nearly equivalent. It istherefore demonstrated that the proposed K-SVD based artifactremoval technique performs equivalent to FORCe method.

TABLE V: Comparison of average RMSE, CC, SAR and MI values of theProposed K-SVD method with FORCe method for all the fourteen dataset.

Metrics K-SVD FORCe


Signal to Artifact Ratio (SAR) 2.0282 2.0565Mutual Information (CC) 1.0102 1.0154

VI. DISCUSSION

1) Phase delay: In our proposed sparsity-based methods,we are processing the EEG signals in time-domain withoutperforming any transformations such as Fourier, wavelet, etc.Also, we are not using any filters for processing the signals.The acquired EEG signals are segmented and directly passedthrough the proposed MCA and K-SVD algorithms to estimateartifact-free EEG signals. Hence, no phase delay involved inthe proposed methods.

To validate our argument, we estimated time-delay betweenthe degraded EEG signal and the artifact-free EEG signal. Thegeneralized cross-correlation (GCC) [51] between the acquiredraw EEG signal x1(t) and the artifact-free EEG signal x2(t)is computed as

GCCx1x2(τ) =

∫ ∞−∞

ψg(f)X1(f)X∗2 (f)ej2πfτdf (13)

where X1(f) and X2(f) are the Fourier transform of the sig-nals x1(t) and x2(t), respectively, and ψg(f) is the weighting




function and it is chosen as ψg(f) = |X1(f)X∗2 (f)|−1. Fig. 14shows the GCC of the degraded EEG signal and the estimatedartifact-free EEG signal (300 samples) and we observe notime-delay between the signals.

-300 -200 -100 0 100 200 300Lag Times (ms)

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Fig. 14: Generalized cross-correlation (GCC) of the acquired raw EEGsignal and the artifact-free EEG signal using K-SVD method for the subject

S1 1. The time-delay is 0 samples between the two EEG signals.

2) Computation time: The speed of the algorithm is animportant issue in real-time BCI applications. In this work,we have compared the execution times of each algorithm. Theproposed sparsity-based algorithm and FORCe algorithm havebeen executed on the same PC with 3.2 GHz CPU, 4 GB RAMand software (MATLAB R2015b). MCA took an averageexecution time of 24.32s, K-SVD requires 31.02s and FORCeconsumes 27.48s for executing all the 14 channels. MCAmethod provides lesser execution time since it uses pre-defineddictionaries. In recent literature [52], [53], the original K-SVDalgorithm has been modified to perform parallel updation ofatoms with less computation time. For the sake of simplicityand in order to provide a benchmark comparison, we used theoriginal K-SVD algorithm with sequential updation of atoms.

VII. CONCLUSION

In this paper, we proposed two sparsity-based approaches,namely, MCA and K-SVD to remove EB artifacts from theraw EEG signal. The advantage of the proposed K-SVD basedmethod is that the learned dictionary adaptively estimatesthe EB artifact from the given EEG signal. On the otherhand, if the pre-defined basis functions are not chosen ap-propriately in MCA method, the morphological componentsof the given EEG signal may not be modeled accurately.The experiments on synthetic and real EEG data demonstratethat learning the dictionary from the given data using K-SVD algorithm yields superior performance as compared tousing pre-defined dictionaries in the MCA algorithm. Theproposed K-SVD algorithm is also compared with the recentstate-of-the-art EEG artifact removal method FORCe [15].Experimental results show that our K-SVD based artifactremoval algorithm performs equivalent to the FORCe method.The proposed sparsity-based methods do not require anychannel information, parameter tuning such as thresholding oradditional equipments/EOG channels to remove EB artifactsfrom the EEG signal. Although, the algorithms proposed inthis paper are intended only for EB artifact correction, theycan also be applied to other artifacts in raw EEG data.

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Sreeja. S. R received her B.Tech degree in Infor-mation Technology and M.E degree in ComputerScience Engineering from Anna University, Chen-nai, India, in 2010 and 2014, respectively. Presently,pursuing her Ph.D in the Department of ComputerScience Engineering in Indian Institute of Technol-ogy, Kharagpur, India. Her current research interestsare artifact removal from EEG signals and brain-computer interface.

Rajiv Ranjan Sahay received the B.Tech. degree inElectrical Engineering from the National Institute ofTechnology, Hamirpur, India, in 1998, the M.Tech.in Electronics and Communication Engineering fromthe Indian Institute of Technology, Guwahati, India,in 2001, and the Ph.D. degree from the IndianInstitute of Technology, Madras, in 2009. He wasemployed as a research engineer at the Indian SpaceResearch Organization (ISRO) from 2001 to 2003.He was a postdoctoral researcher at the School ofComputing, National University of Sinapore during

2010-2011. He is currently an Assistant Professor in the Department ofElectrical Engineering at the Indian Institute of Technology Kharagpur, India.His research interests include 3-D shape reconstruction using real-aperturecameras, super-resolution, and machine learning. His dissertation investigatedseveral extensions to the shape-from-focus technique including exploitingdefocus and parallax effects in a sequence of images.

Debasis Samanta received his Ph.D. degree in Com-puter Science and Engineering from Indian Instituteof Technology Kharagpur, India. He holds M.Tech.and B.Tech. degrees both in Computer Scienceand Engineering from Jadavpur University, Kolkata,India and Calcutta University, India, respectively.Presently, he is an Associate Professor, Departmentof Computer Science and Engineering, Indian Insti-tute of Technology Kharagpur. His current researchincludes Human Computer Interaction, Brain Com-puting Intercation, Biometric-based System Security,

and Data Analytics. For detail, please see http://cse.iitkgp.ac.in/∼dsamanta/

Pabitra Mitra received the B.Tech. degree in elec-trical engineering from IIT Kharagpur, Kharagpur,India, and the Ph.D. degree from the Indian Statisti-cal Institute, Kolkata, India. He is currently an As-sociate Professor with IIT Kharagpur. His researchinterests include pattern recognition and machinelearning.

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