INTRODUCTION
The Electroencephalography (EEG) is a medical instrument that measures the
electrical activity of the brain which are mixed complex spatiotemporal signals
and have several sources that lead to the complexity in the identification process.
EEG data contaminated by different types of artifacts during the recording process.
Almost the biologically artifacts are more worried than external artifacts,
Improving and developing technology can decrease the external artifacts, like
line noise artifact, but biological artifact signals must be extracted and removed
after the recording process (Knight, 2003).
Artifact removal is very necessary to make easier EEG data for representation
and interpretation of the brain signal perfectly to perform a suitable function
in brain computer interface system (Kumar et al.,
2008). Ocular Artifact (OA) is the electrical signals produced by Eyeblinks
or movement of eyeballs. These artifacts in millivolts and they contaminate
the EEG signals which are in microvolts, also the frequency range of EEG signal
is 0 to 64 Hz and the OA occur within the range of 0 to 16 Hz (Krishnaveni
el al., 2006a). The removing process of OA from EEG signals is very
important for automated and visual analysis of implied brain wave activity.
These artifact sources increase the difficulty in analyzing and interpreting
of the recorded EEG data, therefore it is very important to design a procedure
to decrease or eliminate these artifacts from brain signals (Kumar
et al., 2008).
Most individuals seen in recent years, the blind source separation BSS techniques
have a significant and formal area attentions in biomedical signal analysis
(Xue et al., 2006). The most considerable method
is Independent Component Analysis (ICA) as a statistical method to extract and
separates the independent components ICs of the signal. ICA is based on random
and natural gradient (Sun el al., 2003; Mavaddaty
and Ebrahimzadeh, 2011). The famous methods estimate the ICA model by maximizing
the NonGaussianity (Hyvarinen, 1999a), minimizing mutual
information (Comon, 1994), maximizing Likelihood Estimation
(Hyvarinen, 1999b) and JADE algorithms (Cardoso
and Souloumiac, 1993).
A class of secondorder statistic method called Stone’s temporal predictability
method is proposed by Stone (Stone, 2001) with a view
to minimize the probability density functions of the source signals. Automatic
removal of electroocular artifacts from EEG data procedure based on Blind Source
Separation (BSS) is presented in Joyce et al. (2004).
Two ICA algorithms InfoMax (IICA) and ExtendedInfoMax (EIICA) were utilized
to extract eye movements and power noise of 50 Hz in EEG data is proposed by
Xue et al. (2006), it is proven that (EIICA)
method can isolate both superguassian artifacts (Eye blinks) and subguassian
interference (line noise), but (IICA) method is only restricted to remove superguassian
artifacts (eye blinks).
Given the importance of this aspect, the soft computing and intelligent techniques
are studied to remove Electro Oculo Gram (EOG) artifacts and extract useful
EEG data, such as in Kumar et al. (2008). Since
an Ocular Artifact in the EEG data is removed based on Wavelet Transform (WT),
also in Chambayil et al. (2010) Artificial Neural
Network (ANN) is trained to detect EOG artifact and focused on eye blink detection
using kurtosis.
The main purpose of this study, using Stone’s BSS method as compared to
an ICA in isolating the ocular artifacts and correct the EEG data.
OCULAR ARTIFACTS IN EEG
Electroencephalogram (EEG) is a biological signal that demonstrates the electrical
activity of the brain measured by placing electrodes on the scalp (Jain
et al., 2012). An artifact is considered as a disturbance in a measured
brain signal not produced from the brains. Generally can be classified the artifacts
into external and internal categories.
EEG signals often contaminated by strong Ocular Artifacts (OA) produced from
eye movements and eye blinks especially in the EEG recorded from frontal and
frontpolar channels (FP1, FP2, F7 and F8) (Krishnaveni
et al., 2006b). The eye and brain activities have physiologically
separate sources, so the recorded EEG is a superposition of the true EEG and
some portion of the EOG signal (Krishnaveni et al.,
2006a). The interpretation of eye related signals, a downward peak at the
negative peak shows an event of eyesopen and a positive peak show an event
of eyesclose. Also the amplitude of these peaks will be significantly higher
compared to the brain signal as shown in Fig. 1 (Chambayil
et al., 2010).

Fig. 1: 
Contaminated EEG signal by eye blinks 
BLIND SOURCE SEPARATION
Blind Source Separation (BSS), also known as blind signal separation, is a
method of separating the underlying source signals from the observations (mixed
signals), which are the mixtures of the original sources, without the aid of
information (or with very little information) about the original sources or
the mixing process (Tahir, 2010). In a noninvasive technique
(EEG), the sensors (electrodes) are sited at the surface or around the head
(scalp) at very close distance. For each action of the human, a lot of numbers
of sources (neurons) are active (stimulus). Each sensor (electrode) is measuring
a mixture of these stimuli from sources and each sensor measures a different
mixture depending upon its distance from the sources as shown in Fig.
2.
As these are noninvasive techniques, no idea about the sources and the mixing
process that has occurred inside the head. Therefore, cerebral signal
analysis can be considered as a blind source separation BSS problem. EEG data
represent a projection of a set of signals, which are a mixing of cerebral and
artifact signals, onto the sensor (electrode) sites. BSS reduces mixtures of
neural and nonneural variables to independent components of each other. Different
methods of measuring independence provide different BSS algorithms (Joyce
et al., 2004).
ICA is a linear transform of multidimensional data designed to make the output
vectors as statistically independent as possible. ICA is used to separate unknown
source signals from their linear mixture and extract the features (Lisha
et al., 2005).
The schematic diagram for mixing and separation process in BSS is drawn in
Fig. 3 (Abdullah et al., 2012).
Typical linear mixing model of BSS with m observed mixtures (x_{1}(k),
x_{1}(k), ..., x_{m}(k)) of an independent component (s_{1}(k),
s_{1}(k), ..., s_{n}(k)):

Fig. 2: 
Brain signal analysis: Blind source separation problem (Tahir,
2010) 

Fig. 3: 
Mixing and separation scheme in BSS 
They can be written as follows:
Where:
where, Superscript T refers transpose operator; AεR^{mxn} refers
mixing matrix. Symbol (k) is time or sample index.
And finally the separating model is:
where, E is a permutation and scaling matrix and the recovered sources is:
BSS problem is to estimate the best separating matrix W, that ideally equal
to A^{1}.
A new method was proposed by Stone (2001), for source
separation belong to a kind of secondorder statistic method, called by Stone’s
BSS, based on the property for the signal and established on the conjecture
that: “the Temporal Predictability (TP) of any mixture is less than (or
equal to) that of any of its components” (Stone, 2001).
Generally there are three useful properties of the signals:
• 
A Gaussian probability density function based on the central
limit theorem 
• 
Degree of statistical independence 
• 
Temporal predictability 
The first two properties (1 and 2) have previously been used as a base for
the separation but in stone’s BSS only the 3rd property has been used for
the separation. Stone expects this method may be useful in the analysis of medical
applications (Stone, 2001; Abdullah
et al., 2012).
However, Stone’s conjecture according to Xie et
al. (2005) is incorrect and modified, then can be considered the modified
conjecture as a theoretical basis for BSS problem. Another view by Ye
and Li (2007) has introduced a temporal predictability measure based on
difference and on the fact that temporal predictability of the signals is predominantly
different. By using the difference measure, the BSS problem is simplified to
a standard symmetric Eigen problem and the separation matrix is the eigenvector
matrix.
Fast Genetic Algorithm (FGA) is used with the modified stone’s BSS method
to generate and tune Halflife (h_{L}, h_{S}) parameters, which
used by Stone’s method, to enhance the separation process and this algorithm
is based on the responses of two different linear scalar filters to the same
set of signals (Abdullah et al., 2012).
Recently, significant research developed to modify Stone’s BSS method
to solve BSS problem; Stone’s BSS method based on shortterm and longterm
predictors. Trials for a weight vector which backup orthogonal projection of
signals such that each extracted signal is maximally predictable. It’s
a batch method with low complexity. Stone’s measure of Temporal Predictability
(TP) for Nsampled signal (Stone, 2001) is:
where, y(k) is the signal value at time k. U_{y} contemplates the extent
to which y(k) is predicted by shortterm moving average (y_{s}). In
contrast, the term V_{y} is a measure of the overall variability in
y that is measured by the extent to which y(k) is predicted by longterm moving
average (y_{s}). Predicted values y(k), y_{s}(k) of y(k) are
both exponentially weighted sums of signal values measured up to time (k1),
such that recent values have a larger weighting than that indistinct past:
According to Stone’s method, βs.βL ε[0,1] are two different
parameters and y_{l}(1) = y_{s}(1) = y(1). Halflife h_{L}
of β_{L} is much longer (typically 100 times longer) than corresponding
halflife h_{S} of β_{s} and the relation is:
Stone has presented TP of y_{i} for ith extracted signal with separator
vector w_{i} as Rayleigh’s
entropy as follows:
where,
are signal error covariance matrices of mixed predictions by longterm and shortterm
predictors, respectively. Stone’s BSS aims to maximize Rayleigh’s
entropy to yield unmixing vectors. Later, generalized eigenvectors of
are considered as unmixing vectors in Stone’s BSS (Stone,
2001). Another TP measure is presented by Ye and Li
(2007) in the same strategy of Stone’s measure as mentioned above:
For signal series y(k) with zeromean, the covariance C difference is defined
as:
Where:
R_{y} implies the difference measure in the mean value sense. In this
measure, the blind source separation problem is changed to the standard symmetric
Eigen problem and separation matrix is orthogonal (Abdullah
et al., 2012).
MODIFIED STONE’S BSS ALGORITHM
The modified Stone’s BSS method based on Ye and Li
(2007) interpretation is presented in this section and explanation, how
Stone’s BSS deploys generalized Eigenvalue decomposition to obtain the
unmixing matrix; proposed theory is based on the responses of two different
linear scalar filters to the same set of signals. Indeed, the response of linear
filter to signals is a comprehensive case which includes shortterm and longterm
linear predictors used by Stone’s BSS too. Linear filters are assumed as
scalar filters rather than matrix filters unless stated otherwise (Abdullah
et al., 2012). Figure 4 shows the schematic diagram
for the theoretical foundation of the modified Stone’s BSS method.
Where that:
• 
X (k) = Mixture observation signals 
• 
X_{L} (k) = Filter Response (L) 
• 
X_{S} (k) = Filter Response (S) 
• 
= Longterm covariance matrix 
• 
= Shortterm covariance matrix 
• 
R_{XX} = 
• 
V = Eigenvector matrix R_{XX}V = VD 
• 
W = Unmixing matrix 

Fig. 4: 
Schematic diagram of modified Stone’s BSS method 
The preprocessing of BSS consists of (Centering and Whitening), from the most
basic and necessary preprocessing is to center the received observation mixture
of signals x, i.e., subtracts its mean vector m = E [x] so as to make x zeromean.
Another useful preprocessing strategy called whitening; the observed vector
x linearly transforms to obtain a new vector
which is white, i.e., its components are uncorrelated with unity variances or
covariance matrix of
equals the identity matrix (Hyvarinen, 1999a):
Eigenvalue decomposition (EVD) of the covariance matrix is a popular method
for whitening. From Cichocki and Amari (2005), vector
of m mixture X(k) = [x_{1}(k),..., x_{m}(k)]^{T} has
been received by m sensors. BSS considers the best estimate of unmixing matrix
W_{mxn} in order to estimate n unknown sources as mentioned by Hyvarinen
(1999b):
Y(k) = [y_{1 }(k), y_{2 }(k) , ..., y_{n}
(k)]^{T}
X_{L}(k) and X_{S}(k) are, respectively responses of two different
linear filters L and S to estimated (recovered) the signals by W. From Ye
and Li (2007) some plausible assumptions and properties refer to estimate
W such as:
• 
Assumption 1: Mixing matrix is full column rank 
• 
Assumption 2: Sources are mutually uncorrelated and autocorrelation
functions are not equals 
• 
Assumption 3: Responses of sources to first filter (L) are not
the same from their responses to the second filter (S) 
• 
Assumption 4: Unmixing matrix is orthogonal separating 
• 
Property 1: If sources signals are mutually uncorrelated then response
of linear filter are also mutually uncorrelated and covariance matrix of
the response signals is a diagonal matrix 
• 
Property 2: If
are, respectively responses of a linear filter to y(k) and x(k): 
The covariance matrices of X_{L}(k) and X_{S}(k) are diagonal
matrices because the source signals S(k) are mutually uncorrelated (Assumption
2):
As shown
are distinct diagonal matrices their multiplication R_{XX }is a diagonal
matrix:
Or can be said:
From Eq. 15 and 16:
Now, can be representing the problem as generalized eigenvalue decomposition
(Stone, 2001) and from the Assumption 4:
Then, the unmixing matrix W is organized of the eigenvector matrix of Eq.
23, also they are orthogonal. Halflife h_{L} of β_{L}
is much longer (typically 100 times longer), subsequently these values are being
affected on the responses of two linear filters . Since two employed linear
filters are error terms of shortlength and longlength prediction.
SIMULATION RESULTS
The EEG signals are recorded and processed firstly by temporal filter (FIR
filter) and then by spatial filter (Blind source separation) techniques: FICA,
JADE, EIICA and Stone’s BSS. In order to compare the results with previous
studies, as in Salim (2007), the same procedure is taken
but with different blind source separation technique, also the simulation result
compared with ExtendedInfomax (EIICA) as demonstrated by Xue
et al. (2006). In this study a modified Stone’s BSS method
is used instead of Independent Component Analysis (ICA) algorithms. To illustrate
experimental demonstration of validity of the BSS method in details, the procedure
is divided into three steps:
Step 1: EEG signal acquisition: One healthy subject, male, 24 years
old was participated in the work. EEG signals were measured using a computerized
EEG device (Fig. 5), 19 electrodes, 1020 international system
and referenced against forehead (Fig. 6) in IbnRushd HospitalBaghdadIraq
for more details see Salim (2007).
According to the specification of the computerized EEG device the recorded
signals were digitized at 256 Hz. The trail length is 10 sec (10 secx256 Hz
= 2560 samples), during which the subject was allowed to perform random artifacts
(eyes blinking) before 3rd sec. At 3rd sec the subject stays quiet and stays
without any action. From 5th to 6th sec, the subject performs the left or right
hand index movement depending on the part of the session Salim
(2007).
The effect of ocular artifacts will be dominant in the Frontal and Frontopolar
channels like FP1, FP2, F7 and F8 (Krishnaveni et al.,
2006b). Figure 7, show the contaminated EEG signals by
eye blinks for frontal and frontpolar channels FP1, FP2, F7 and F8.
Step 2: Filtering process using temporal filter: Generally the first
step for EEG signal processing is a filtering process to remove subguassian
interference (line noise), DC. Drift and reduce superguassian artifacts (eye
blinks) (Xue et al., 2006; Salim,
2007). Here, a temporal filter is used with 545 Hz bandpass filter and
implemented by a WindowedSinc FIR filter with a sampling rate of 256 sample
sec^{1} and filter kernel length M of 1024 calculated according to:
where, BW is a width of transition band.

Fig. 5: 
Computerized EEG system (IbnRushd HospitalBaghdadIraq) 

Fig. 6: 
The 1020 International EEG electrode configuration system 

Fig. 7: 
Contaminated EEG signal for (a) FP1, (b) FP2, (c) F7 and
(d) F8, channels 
A Blackman window has been used in this implementation. The filter kernel of
the lowpass filter is calculated according to:
where, h[i] is a filter kernel, k is a filter gain, M is the kernel length
filter, f_{c} is a cutoff frequency and i is the index. The algorithm
of calculating the filter kernel of the bandpass filter with cutoff frequencies
of f_{c1} = 5 Hz and f_{c2} = 45 Hz is shown below (Salim,
2007):
• 
Values confirmation: let M = 1025, S_{rate} = 256,
f_{1} = f_{c1}/S_{rate}, f_{2} = f_{c2}/
S_{rate} 
• 
Calculate lowpass filter kernel at f_{1} 
• 
Calculate lowpass filter kernel at f_{2} 
• 
Normalize both filter kernels 
• 
Change the lowpass filter kernel of hh to highpass filter using spectral
inversion 
• 
Add the lowpass filter kernel hl to the high filter kernel hh to obtain
a bandreject filter kernel 
• 
Change the bandreject filter kernel to a bandpass filter using spectral
inversion 
The filtered signal is obtained by convolve the input signal with the filter
kernel. Figure 8 shows the effect of temporal filters (WindowedSinc
FIR filter) to reduce the amplitude of the ocular artifact.
Step 3: Processing the EEG data by BSS: Rejecting contaminated trials
causes substantial data loss and restricting eye blinks limits the experimental
designs possible and may impact the cognitive processes under investigation
(Kumar et al., 2008). In this step, use Stone’s
BSS to separate the ocular artifacts from filtered EEG signals.
Figure 9 shows the Independent Components (ICs) found by
Stone’s BSS method and it can
be clearly found from this figure that the pure eye blink artifacts were isolated
in IC10 and IC18 successfully, as indicated by potential contour maps in Fig.
10. The EEG signals are cleaned from an artifact and then can be use this
data for classification and extract the main features as a next step in Brain
Computer Interface (BCI).
Stone’s BSS method has been
compared with wellknown BSS algorithms as shown in Fig. 11.
The signal has been taken until the 3rd sec (256x3 = 768 samples) which is set
by the presence of Eye artifact to compare the resulting curves produced by
different methods for FP1 channel. Clearly stone’s
method is the best from other by extracting the brain signals and isolate the
eye blink artifacts.

Fig. 8: 
Reduced contaminated EEG signals using WindowedSinc FIR filter
for (a) FP1, (b) FP2, (c) F7 and (d) F8, channels 



Fig. 9:(as) 
19 independent component of EEG signals using Stone’s
BSS, Xaxis represent signal Amplitude in microvolt and Yaxis represent
No. of samples (a) IC 1, (b) IC 2, (c) IC 3, (d) IC 4, (e) IC 5, (f) IC
6, (g) IC 7, (h) IC 8, (i) IC 9, (j) IC 10, (k) IC 11, (l) IC 12, (m) IC
13, (n) IC 14, (o) IC 15, (p) IC 16, (q) IC 17, (r) IC 18 and (s) IC 19 

Fig. 10(ab): 
(a) Potential contour map IC10 (b) Potential contour map
IC18 


Fig. 11(af): 
EEG signals for frontal channel (FP1) (a)
Raw EEG signal, (b) After Band pass filter, (c) After Stone’s BSS,
(d) After FICA, (e) After JADE and (f) After EIICA 

Fig. 12: 
Spectra of frontal channel (FP1) computed using power spectrum
from Raw EEG signal and different types of BSS techniques 
One of the most important properties of eye blink artifact, it has lower frequency
components compared with the EEG data (Hellyar et al.,
1995). Therefore, can be exploited this feature to identify the eye blink
artifacts also the time domain features are more suitable for this type of artifact
(Yoo et al., 2007).
Figure 12 shows spectra of Frontal channel (FP1) computed
using power spectrum from Raw EEG signal and different types of BSS techniques
(Stone’s, FICA, JADE, EIICA). As shown in Table 1 the
values of total power spectrum for eye blink artifact without BSS methods are
absolutely high but its very low when Stone’s BSS approach is used.
Table 1: 
Total power value of frontal channel (FP1) with and without
BSS techniques 

CONCLUSION
This study introduces, a method to separate the eye blinks artifacts based
on Stone blind source separation method, hence it’s
proved that, the Stone’s BSS
method is an efficient method to separate completely these artifacts from EEG
signal without removing significant and useful information (data). However,
BSS based on independent component analysis has gained a great deal of popularity
in the bio signal analysis, but it has some limitations, therefore must be developed
another method like the Stone’s
BSS method to decrease the limitation and minimize the complexity of the work
. In all cases, artifacts were adequately attenuated. It is concluded that the
Stone’s BSS method gives less
complexity, easy to separate the artifacts and is an efficient technique for
improving the quality of EEG signals in biomedical analysis.
The contribution presented here, use a Stone’s
method to separate an ocular artifact in EEG signal, i.e., a new application
for this method in brain signal analysis, as expected in Stone’s
study. In the proposed method, a temporal filter (WindowedSinc FIR filter)
is used to remove subguassian interference (line noise), DC. Drift. It can be
seen that, this type of filter is very good for this purpose but can’t
remove a superguassian artifact (eye blinks) only reduce it, and then the spatial
filter is used to separate completely these artifacts. Finally the Stone’s
BSS method holds promise toward brain signal analysis and a quite powerful technique
and suitable for EEG signal processing in clinical engineering.
Open problem and suggestions: A great challenge in the brain signal
analysis is for noninvasively assess the physiological changes take place in
various parts of the brain. To extract the pertinent information (data) for
diagnosis, expert knowledge not only in medicine but also in statistical signal
processing analysis is required. In brain signal processing, one important task
is how to automatically detect, extract and eliminate noise and artifacts, then
how to enhance the extracted signals and classify the brain sources.
Numerous artifacts may appear in EEG signals such as potentials related to
cardiac activity Ballisto Cardio Gram (BCG), Myogenic potentials Electro Gyo
Gram (EMG) artifacts. It can be expected, the Stone’s
BSS is a useful method for these artifacts. Also can be extended this study
by using various soft computing techniques with a Stone’s
BSS method to produce a modified method.
The next step is a classification process to extract main features of cleaned
EEG signals and to determine the mental task which used in Brain Computer Interface
system (BCI).
ACKNOWLEDGMENT
This study is funded by an international exchange program of Harbin Engineering
University for innovation oriented talents cultivation.