By modeling the signal acquired at each electrode of the EEG measurement system as a linear combination of source signals generated in the brain, we can apply Blind Source Separation BSS
Trang 1An Effective Procedure for Reducing EOG and
EMG Artefacts from EEG Signals
Nguyen Thi Anh-Dao‡, Tran Duc-Nghia††, Nguyen Thi-Hao†, Tran Duc-Tan† and Nguyen Linh-Trung†
emails: linhtrung@vnu.edu.vn
Abstract—Epilepsy is a neural disorder in which the electrical
discharge in the brain is abnormal, synchronized and excessive
Scalp Electroencephalogram (EEG) is often used in the diagnosis
and treatment of epilepsy by examining the epileptic seizures and
epileptic spikes By modeling the signal acquired at each electrode
of the EEG measurement system as a linear combination of
source signals generated in the brain, we can apply Blind Source
Separation (BSS) techniques to separate the brain activity from
other activities In this paper, we concentrate on applying
Second-Order Blind Identification (SOBI) algorithm to remove eye
(EOG) and muscular (EMG) artifacts However, the disadvantage
of SOBI is that it cannot provide the information about the order
of sources, thus, an identification procedures of artifacts is further
needed The effectiveness of this method has been examined and
verified by simulated and experiment data
Index Terms—epileptic seizures, EEG, EOG, EMG,
time-frequency representations, under-determined blind separation
I INTRODUCTION
Epilepsy is a neural disorder characterized by an enduring
predisposition to generate epileptic seizures and its
neurobio-logic, cognitive, psychological, and social consequences An
epileptic seizure is the abnormal, synchronized and excessive
electrical discharge in the brain [1] Scalp
electroencephalo-gram (EEG) is the recording of the temporal electrical brain
activity through a set of scalp electrodes, thus it is useful
to localize epileptogenic zones However, the EEG-based
epileptic diagnosis faces difficulty because EEG signals are
often disturbed by artifacts, such as: eye movements, muscle
activity and heart activity
Each recorded EEG signal can be modeled as a linear
combination of source signals generated in the brain (seizures,
background neural activity, artifacts, etc) Because it is not
possible to turn off either the artifact sources or the cerebral
sources it is not possible to record either or alone The artifact,
thus, has to be removed from the combined recording by
means of signal processing This has led to the development
of several correction algorithms Hence, it is appropriate to we
can apply blind source separation (BSS) techniques to separate
the seizure from other signals in the EEG data BSS aims at
recovering several source signals from several observed only
linear mixtures of the source signals while the information
about the mixing system is unavailable Assumptions for BSS
to work depend on the mixing model and statistical properties
of the source signals
Second-Order Blind Identification (SOBI) is one of the existing well-known and effective BSS algorithms and it has been applied widely in many applications [2] Some automatic detection methods based on SOBI and extended versions of SOBI have been proposed in previous studies [3]–[6] For examples, in [4], automatic removal of eye movement and blink artifacts from EEG data were considered In [5], spatially constrained ICA algorithms were proposed with applications
in EEG processing These works are mostly complicated and time consuming In this paper, we proposed an effective scheme to remove electrooculographic (EOG) and electromyo-graphic (EMG) artifacts from EEG This scheme works based
on SOBI The disadvantage of SOBI is that it cannot provide the information about the order of sources Therefore, an identification procedures of artifacts is further needed
A Noisy EEG data
Within the scope of this research, we study two epileptic EEG dataset– simulated and real– disturbed by an eye-blink artifact, which is the most popular factor of distortion in the EEG recording process The first one contains four simulated EEG signals each of which is a mixture of three sources: EEG background, eye-blink artifact and seizure
Ocular artifacts are the most relevant interference because they occur very frequently and their amplitude can be several times larger than brain scalp potentials As the eyeball moves, the electric field composed by cornea and retina changes and it produces the electrooculographic (EOG) signals Additionally, some neural activity is recorded by EOG electrodes because they are located near the head
B Blind Source Separation and SOBI algorithm
The assumption of independence among source signals can
be relaxed to uncorrelation while using additional information about the source autocorrelation Thanks to using only second order statistic (SOS) information, the complexity of the SOBI algorithm and signal length can be reduced These algorithms have previously been applied to EEG seizure separation The classical linear mixing model can be written, at each instantk, as:
Trang 2wherex is a vector of M observed signals in EEG channels, A
is the unknown full-column rank mixing matrix whose size is
M×N and s is the vector of N independent unknown sources.
channels is larger than number of sources Thus, the separation
problem here has a solution To estimate the original sources,
it is need to calculate the following linear transformation:
linear transformation matrix that allows the separation of the
be prefectly recovered The most currently employed solution
is to evaluate the number of linearly independent measures in
the mixture by using some criterion based on the eigen-values
of the covariance matrix of the measured signals However, it
is difficult to obtain the exact inverse of the mixing matrixA.
It means that the sources can be recovered without information
of their order and amplitudes
In practical applications, we should not ignore the noise
Therefore, Eq (1) should be rewritten as
Several BSS algorithms have been proposed and analyzed
during the last decades Globally, source separation algorithms
lay into two categories: those based on High Order Statistics
(HOS) and those based on Second Order statistics (SOS)
SOBI is one of the most representative algorithms of the SOS
family The main advantages of these algorithms are their
hypothesis are a priori verified for real EEG signals, which
are band-limited and noisy These algorithms were already
successfully applied for EEG separation, for example in [7],
[8] Thus, we have included it into our analysis
The first step of SOBI consists of whitening the signal part
the joint diagonalizer ˆU of a set of covariance matrices Due to
the whitening process, ˆU is unitary Then, the mixing matrix
can be calculated by the multiplication of pseudo-inverse of
the whitening matrix with the diagonalizing matrix ˆA= ˆ W#U.ˆ
Finally, the source signals are estimated asˆs(t)= ˆA#ˆx(t).
C Proposed scheme
The disadvantage of SOBI is that we can not obtain the
information of the order of sources and, thus, we can not
compensate the noisy signal correctly In our study, we propose
a method that integrates SOBI and source identification in
order to remove EOG and EMG out of noisy EEG signals
Expanding Eq (1) to
x1(k) = a11s1(k) + a12s2(k) + · · · + a 1N s M (k)
x2(k) = a21s1(k) + a22s2(k) + · · · + a 2N s M (k)
x M (k) = a M1 s1(k) + a M2 s2(k) + · · · + a MN s M (k)
(4)
EEG signals will be compensated as in the following:
x
1(k) = x1(k) − a11s1(k) − a12s2(k)
x
2(k) = x2(k) − a21s1(k) − a22s2(k)
x
M (k) = x M (k) − a M1 s1(k) − a M2 s2(k)
(5)
signals of the SOBI block is limited In the frequency domain, the energy of an EOG signal is maximum at low frequency while that of an EMG is maximum at high frequency Thus,
we have exploited these properties to identify EOG and EMG Our scheme can be summarized in Algorithm 1
Algorithm 1 EOG and EMG identification using moving window
Step 1: Initiate a moving window whose size isN=1000 ms.
Step 2: Calculate the weighting parameter for all channel
w=E(f > 55Hz)/E(f < 20Hz).
Step 3: EOG channel is determined by minimum ofw and EMG
channel is determined by maximum ofw.
Step 4: Compensate the noisy EEG channel by using the Eq (5) Step 5: Continue with the next window
20Hz) instead of the energy is that the energy may be varied
from channel to channel
A Simulated Results
Figure 1 shows an EEG signal without any artifacts We can see that there are two spike existed in this segment of
amount of additive noise was added to this signal
samples are EOG After that, the EEG is mixed with the EOG
signal is shown in Figure 3 It is easy to see that the EEG signal
is now dominated by EOG Consequently, we will analyze the performance of EOG removal using several methods: Least Mean Square (LMS), Zhou [9], Total variation (TV), and our method (e.g., combination of SOBI and identification of artefacts)
Figures 4, 5, 6, and 7 show the filtered signal applied to the first mixed one by LMS, Zhou, TV, and our proposed method, respectively Using LMS can amplify two spikes but it could not remove EOG artifacts Results obtained by Zhou or TV are even worse On the other hand, our method offers very good results wherein it can eliminate the EOG artifact totally
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Fig 1 EEG signal without artifacts
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Fig 2 EOG signal affected in 2000 first samples
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Fig 3 The mixed signal between EEG and EOG
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Fig 4 The filtered signal using LMS algorithm
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Fig 5 The filtered signal using Zhou algorithm
3200th positions.
Similar to EOG, the simulation scenario is changed to mixing of the EEG activity with an EMG artefact We can also obtain a good result using our method, in comparison
before and after filtering using our method
B Experiment Results
on the hard disk for further processing The initial processing
filter and a band-pass filter that altogether pass the signal
We choose the first input channel is the channel that is
Figure 9 shows the signals in4 channels affected by EOG It is obviously that the EOG is dominated in all four channels from
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Fig 6 The filtered signal using TV algorithm
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Fig 7 The filtered signal using our algorithm
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before filtering after filtering
Fig 8 The signal at channel 4 before and after filtering using our method
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Fig 9 The signal at 4 channels affected by EOG
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Samples Fig 10 The signal at 4 channels filtered by Zhou
1thto450thsamples Figures 10 and 11 show the signals in
4 channels filtered by Zhou and our method, respectively We can see that the EOG artefacts are eliminated in all channels
channels are larger than ones using Zhou’s method
For EMG artefact, we choose the first input channel is the
and F p1) Figure 12 shows signals in 4 channels affected
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Samples Fig 11 The signal at 4 channels filtered by our method
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Fig 12 Signals in 4 channels affected by EMG
by an EMG artefact These signals are treated by using our
method The recovered signals are shown in Figure 13 It is
very interesting to see that in Figure 12 we can not realize
spikes are very clear, specially at three first channels
This paper presents a new approach for minimization of
EOG and EMG artefacts from EEG signals The simulation
and experiment results demonstrated that our method shows
the better performance comparison with some conventional
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Fig 13 The signal at 4 channels filtered by our method
ones In the future works, we will integrate the EMD method with this algorithm to enhance the performance
ACKNOWLEDGMENT
This work was supported by Project QG-10.40 granted by Vietnam National University Hanoi
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