Improved Algorithm for Brain Signal Analysis

*Corresponding Author: Dr. A. Rajamani, Email: rajamani_saranya@rediffmail.com
RESEARCH ARTICLE
www.ajcse.info
Asian Journal of Computer Science Engineering2017; 2(6):22-28
Improved Algorithm for Brain Signal Analysis
*Dr.A.Rajamani1
, Ms.N.Saranya2
1, 2*
Department of ECE, PSG Polytechnic College, Coimbatore, India
Received on: 11/10/2017, Revised on: 01/12/2017, Accepted on: 30/12/2017
ABSTRACT
Blind Source Separation (BSS) is an approach to extract the meaningful data from the non Gaussian
independent element of the combined sources. The count and the mixing of pattern from the different
sources are not known and hence the name ‘blind’. Joint blind source separation (JBSS) algorithm is
beneficial to get common sources at a time exist across multiple dataset like ElectroEncephaloGram
(EEG). In this research work, extract of the signal from expected independent elements using an effective
algorithm is presented. It is compared with several other BSS algorithms like STFT, ICA, EEMD and
IVA. This analysis also helps to early diagnosis of neurological diseases such as brain hypoxia, epilepsy,
sleep disorders, and Parkinson’s disease etc. The observational results have higher SNR and Average
Correlation Coefficient (ACC) values for the proposed algorithms compared to other BSS techniques.
Keywords: Blind Source Separation (BSS), ElectroEncephaloGram (EEG), ICA, SNR, ACC.
INTRODUCTION
Blind Source Separation techniques are the most
beneficial and common method in signal
processing. In the area of multichannel recording
many techniques of BSS are introduced which
work accurately in contrast to the multichannel
recording in the single channel measurement.2
In
the area of biomedical signals, independent
sources are frequently blended together with the
measured signal. Our job is then to separate
contribution sources in order to have a nearer look
at the signal of interest. In multichannel
recordings such as EEG this problem is efficiently
managed by using blind source separation
techniques to sort the given mixed signal into an
original non mixed sources.3
On EEG techniques,
sensors are pointed at the head surface and large
number sources are active during each human
action. There have no estimate about the origins or
the mixing process of EEG signals in the
cognitive system. Therefore brain signal analysis
to be regaled as a BSS problem.1
Independent
Component Analysis (ICA) is a statistical
technique for recovering statistically independent
sources of mixed data.4
In order to improve the
accuracy and the stability of BSS, the family of
ICA algorithms were implemented to extract the
Independent Components. There are also many
criteria to extract independent component and the
FASTICA algorithm is one of the most well-
known and popular method.5
The algorithm is
established on a fixed point iteration scheme and
maximizing Non-Gaussianity of the component.
To recognize the source signal, many methods are
proposed by the researchers which are classified
based on the field of application. The time domain
BSS method prominently encounters signal
attenuation and permutation problem. Using
Fourier transforms to the time domain convolutive
mixture, commutes to an instantaneous mixture
problem in the frequency domain.6
In the
frequency domain BSS method seldom happens
signal ambiguity (i.e., Scaling and Permutation),
to overcome this problem use time-frequency
domain methods, such as STFT and Wavelet
FASTICA. Wavelet FASTICA breaks down the
signal into different frequency bins.7
Very
recently, independent vector analysis (IVA), as an
extension of ICA from one of multiple data sets,
has drawn increasing attention. IVA was
originally designed to address the permutation
problem in the frequency domain for the
separation of acoustic sources.15
IVA was
formulated as a general JBSS framework to ensure
that the extracted sources are independent within
each dataset and correlated well across multiple
information sets.16
Empirical mode decomposition
(EMD) is a worthy alternative for this
determination. EMD is a single-channel technique
that decomposes a non-stationary and nonlinear

Rajamani et al./ Improved Algorithm For Brain Signal Analysis
© 2015, AJCSE. All Rights Reserved 23
time series into a finite act of intrinsic mode
functions (IMFs).17
Compared with other
decomposition methods (e.g., wavelet transform),
EMD is completely data driven, meaning that it
breaks down a signal in a natural manner without
needing a prior knowledge.18
It has been proven to
be effective in many biomedical applications, e.g.,
denoising electrohysterogram (EHG) signals and
EEG signals.19,20
Nevertheless, the original EMD
algorithm is extremely sensitive to noise and it
causes mode mixing. Lately, a noise-assisted
version of the EMD, called ensemble EMD
(EEMD), was proposed and has been shown to be
more rich in real-life applications.21
In EEG signal
acquisition there is be a probability of getting both
Uni and multidimensional data, to separate these
different types of dimensional data concurrently
EEMD-IVA technique was mainly used.14
But it
has a limitation of poor converging rate which is
rectified by the proposed method. We have
examined the operation of Proposed Algorithm
algorithms and several other BSS methods on both
synthetic data and real EEG data. The
convergence rate of the proposed method is faster
than the normal EEMD-IVA. We first validate it
on simulated data, and then employ it to the real
EEG data collected from the patients. Lastly, we
compare the operations of each method with the
help of ACC and SNR.
EEG SIGNAL AND ELECTRODE SYSTEM
EEG signals are usually acquired using scalp
electrodes; it is placed according to the 10-20
international electrode system depicted in Figure 1
(C). The “10” and “20” refer to the percentage of
the distance between the landmark points namely,
the Nasion, the Inion, and the Preaurical points, as
shown in Figure 1(A) and (B), used to draw the
lines at which intersections, the electrodes are
placed. In other words, given the landmark points,
the electrode positioning is established by looking
at the intersections between communication
channels which are surgically and carnally drawn,
spaced at 10 or 20% of the space between the
landmark points. Since the early research on EEG
analysis, it has been observed that the areas of a
healthy human cortex have their own intrinsic
rhythms in the range of 0.5 − 40Hz. In general,
five main rhythms can be found from an EEG
recording: Delta (δ) 0.5−4Hz, Theta (θ) 4− 8Hz,
Alpha (α) 8 − 14Hz, Beta (β) 14 − 30Hz and
Gamma (γ) over 30Hz. The amount of action in
different EEG frequency bands is quantified
employing spectral analysis techniques. 8
(A) (B)
(C)
Figure 1. Electrode Placement
EXISTING METHODS
During the seventies, EEG analysis implied
interpreting the EEG waveform using descriptive
and heuristic methods.9
in time; various methods
have been employed to analyze several subtle
changes in the EEG signal.
Short Term Fourier Transform- Independent
Component Analysis (STFT-ICA)
The time - frequency response of EEG signal is
efficiently extracted by STFT-ICA. In this method
windowing techniques are applied to the input
signal. The STFT ICA algorithm steps are
described below.10
 Generate mixed signal
)
(
3
,
2
,
1 t
xi
from
dataset
)
(
3
,
2
,
1 t
si
.
 Obtain 𝑋(𝑓, 𝜏)by taking STFT of )
(t
xi .
 Combine time and frequency
information 𝑋𝑓𝜏.
 Take FASTICA on 𝑋𝑓𝜏 , to find mixing
matrix A.
 Calculate demixing matrix W= 𝐴−1
.
Recover the time domain response by computing
the FT and HT (Hilbert transform) or FT-LT
(logarithm-decrement technique) using demixing
matrix
Wavelet ICA
Wavelet decomposition is a time invariant, the
phase relationship does not change after the
AJCSE,
Nov-Dec,
2017,
Vol.
2,
Issue
6

decomposition of the input signal. As a result,
there is no time delay introduced in this operation,
thus it simplifies the algorithm significantly.11
So
after the mixed signal separation wavelet ICA
keeps the time and frequency information as it is.
 Wavelet decomposition: Apply DWT to
every channel of the recordings to obtain
non-overlapping spectra.
 Identification and selection: To identify
and select only the details that contains
frequency components in a specified
range.
 Preprocessing: Preprocessing step
whitening is performed in order to reduce
the data dimensionality and to lighten the
computational charge.
 ICA: Apply the ICA algorithm to the
selected details.
 Demixing: Compute demixing matrix of
the selected frequency components.
 Wavelet reconstruction: Perform the
wavelet reconstruction using the non
selected details and the cleaned details
after ICA step and obtain the artifacts
removed separated signal
Ensemble Empirical Mode Decomposition
(EEMD)
Empirical mode decomposition (EMD) is a
worthy alternative for BSS determination. EMD is
a single-channel technique that decomposes a
non-stationary and nonlinear time series into a
finite act of intrinsic mode functions (IMFs).
Compared with other decomposition methods
(e.g., wavelet transform), EMD is completely data
driven, meaning that it breaks down a signal in a
natural manner without needing prior
knowledge.14
 Identify all extrema of the mixed input
signal )
(t
x .
 Find maximum and minimum of )
(t
x .
 Calculate the mean 𝑚(𝑡) =
max[𝑥(𝑡)]+ min[𝑥(𝑡)]
2
 Extract the details ).
(
)
(
)
( t
m
t
x
t
d 

 Iterate on the residual m(t)
 Fix the standard deviation (0.3-0.4).
Extract IMFs and calculate the ensemble average.
Independent Vector Analysis (IVA)
Independent vector analysis is an extension of
ICA, it deals with multivariate sources. In the
frequency domain the separated signals are
swapped which leads to permutation ambiguity to
overcome this IVA is mainly used. The IVA
model consists of a set of standard ICA models.12
The univariate sources across different layers are
dependent such that can be aligned and grouped as
a multivariate variable. The IVA steps are
described below
 Preprocessing: Whiten the input
signal𝑋𝑓
, to make it as an un-correlated
signal (mean=0, variance=1).
 Mutual information: Find 𝐼(𝑦) =
𝐷(𝑓
𝑦|| ∏ 𝑓𝑦𝑖
)
𝑖 , using the contrast function
and determine the source distribution using
information geometry
 Complex variable: Assume 𝑍 = 𝑢 + 𝑗𝑣,
where 𝑗 = √−1
 Contrast function: Spherically symmetric
exponential norm distribution (SEND),
Gaussian or Laplacian contrast function is
used.
 Contrast optimization: Newton’s rule/
Gradient descent rule is used for
optimization.
 Find the demixing matrix: For each
frequency bin, calculate W to separate the
signal.
PROPOSED ALGORITHM
If the input signal having both Uni and
multidimensional dataset means current existing
algorithms are failing to handle this situation.
Hence, joint Enhanced EEMD and IVA
algorithms proposed to solve this problem. This
proposed method gives better ACC and SNR
value compare to normal EEMD-IVA algorithm
by obtaining an easy separation of the different
dimensional EEG signals. The algorithm steps are
same as mentioned earlier only small corrections
in the contrast function and ensemble average.
Instead of the Gaussian contrast function use LoG
(Laplacian of Gaussian) contrast function, this
gives very fine and accurate separated signals. On
normal EEMD algorithm, ensemble average is
taken at the end of IMFs decomposing, but this
enhanced algorithm ensemble average is taken at
each step to get accurate IMFs. So combine these
two enhanced algorithms to get high ACC and
SNR value. The Proposed Algorithm flow is
shown in figure 2.
AJCSE,
Nov-Dec,
2017,
Vol.
2,
Issue
6

Figure 2.Flowchart of existing and proposed algorithm.
RESULTS AND DISCUSSION
Table1. Non Gaussian (NG) Test of EEG Signal
Regi
on
Mea
n
Standard
Deviation
Variance Skew
ness
Kur
tosis
Conclusion
FP1-
F7
3.91
7578
172.0907 29615.21 0.581
688
9.17
1829
NG
F7-
T7
6.48
3984
114.3495 13075.81 1.132
331
11.4
9368
NG
T7-
P7
2.49
1016
71.80803 5156.394 -
0.009
27
3.87
4516
NG
P7-
O1
1.72
1094
47.70576 2275.839 0.062
697
3.03
0272
NG
FP1-
F3
8.00
3906
175.471 30790.06 1.192
968
13.0
708
NG
F3-
C3
5.47
3047
65.90385 4343.318 0.560
801
6.62
5538
NG
C3-
P3
0.22
813
43.64903 1905.238 0.263
432
3.67
0766
NG
P3-
O1
1.55
3516
52.41709 2747.552
0.152
869
3.60
276
NG
Figure 3.Comparison results of existing and proposed
algorithm.
The EEG signals are non Gaussian in nature; it is
verified with the help of statistical measures. If the
mean value is zero and variance is constant, then
the signal is Gaussian. Skewness returns the
symmetrical measurement. A negative value of
skewness indicates that the left side of the
probability density function is longer than the
right side. The positive value of skewness
indicates that the right side of the probability
density function is longer than the left side. In
kurtosis, normal distribution has a value of three.
A kurtosis value of less than 3 indicates a flatter
distribution than normal. The kurtosis value of
greater than 3 indicates a sharper distribution than
normal.13
eight regions of EEG signals were
investigated and tested for non-gaussianity, and
their measures are given in table 1.
Existing BSS methods like STFT, Wavelet ICA,
EEMD and IVA algorithm results are compared
by the means of waveforms, SNR and average
correlation coefficients (ACC). First, the
algorithm is applied for generating test signals,
and then followed by EEG signals.
As an example, consider three generated signals
and these signals are mixed with a mixing matrix
A.
The following three source signals were seen:
𝑠1 = 1.5cos(0.01t)sin(0.5t) (1)
𝑠2 = 1.5 sin(0.025t) (2)
𝑠3 = 1.5sin(0.025t)sin(0.2t) (3)
Where 𝑠𝑖 is a simulated source and it varied from
i=1, 2,3 and t is a number of samples (T=1000).
These three simulated sources, as shown in Figure
3. Note that here si′s are row vectors. The mixed
datasets were brought forth as follows, with each
column denoting one observation in their
respective data space.
X = A . S (4)
Where S = [s1; s2; s3] With
A = [
0.2590 0.3264 0.6512
0.4522 0.1219 0.7194
0.0855 0.9133 0.9203
] (5)
Where A is a mixing matrix and X is a mixed
signals. The Figure 3 shows the separated signals
in all the types of BSS techniques. In STFT one
particular size of the time window is selected for
all the frequencies, which restricts the flexibilities
of the input signal, but wavelet ICA is flexible in
all the signals so Wavelet ICA decomposes the
signal it may be any form weather it is a one or
multi dimensional signal. But it needs prior
information of the input signal for decomposition.
IVA is applied for each frequency bin of the
mixed sources, it will give the demixing matrix of
the entire frequency bin and also it removes the
permutation ambiguity. Compare to all BSS
techniques IVA will give better SNR and
correlation coefficient value. Likewise the same
BSS algorithm is put on for EEG signals and the
results recorded in Figure 3.
All these methods deal with multidimensional
signals except wavelet ICA, if both Uni and
multidimensional signals are getting concurrently
the EEMD – IVA method is employed to sort out
the signals normally this pattern came in brain
AJCSE,
Nov-Dec,
2017,
Vol.
2,
Issue
6

signal acquisition. But this EEMD-IVA method
has some disadvantages the speed of the
convergence rate is very slow and it will give an
Average correlation coefficient value around 0.8
this is insufficient value to diagnose some
neurological disorders, the separated signals using
EEMD-IVA is shown in Figure 6. So Proposed
Algorithm method is proposed to achieve the
better ACC and SNR value. EEEMD method is to
sort out the one-dimensional signal into a finite
number of IMFs then apply an EIVA algorithm to
tell apart the mixed signals.
As an example, consider five generated signals
and these signals are mixed with a mixing matrix
A.
The following five source signals were seen:
s1 = 1.5 cos(0.01t)sin(0.5t) (6)
s2 = 1.5 sin(0.025t) (7)
s3 = 2cos (0.08t) sin (0.006t) (8)
s4 = 1.5sin(0.025t)sin(0.2t) (9)
s5 = 1.5(sin(0.2t)) (10)
Where 𝑠𝑖 is a simulated source and it varied from
i=1, 2,3,4,5 and t is a number of samples
(T=1000). It is shown in Figure 4. Note that here
si′s are row vectors. The mixed datasets were
brought forth as follows, with each column
denoting one observation in their respective data
space.
X[n] = A[n] · S[n] ,n = 1,2,3 (11)
Where S[1] = [s1; s3; s2] , S[2] = [s2; s4; s5] ,
S[3] = [s1; s3; s4]With
A[1] = [
0.2590 0.3264 0.6512
0.4522 0.1219 0.7194
0.0855 0.9133 0.9203
] (12)
A[2] = [
0.3598 06248 0.7426
0.3821 0.5749 0.8063
0.5320 0.9358 0.2793
] (13)
A[3] = [0.8945 0.7831 0.2763] (14)
Figure 4. Mixed uni and multidimensional EEG signal.
Figure5. Decomposed Unidimensional and EEG Signals by the
proposed algorithm
In this segment, the EEEMD - IVA method is
applied to the synthetic data. This simulation is
used to demonstrate the specific procedure and the
source separation effect of the EEEMD-IVA. In
Figure 4, present the mixed datasets generated
according to equ (14). Since X3 was one-
dimensional, to apply EEEMD to X3 for
decomposition and obtain a lot of averages IMFs,
as indicated in Figure 5. Then the each data set
was multivariate, and to remove irrelevant
redundant information across the multivariate
mixed generated signal datasets.
Figure6. Separated generated and EEG Signals by EEMD-IVA
Figure 7. Separated generated and EEG Signals by Proposed
AJCSE,
Nov-Dec,
2017,
Vol.
2,
Issue
6

Algorithm
Hence the multi-LV method to extract subLVs
from each dataset. It is noted in the beginning;
these subLVs could carry as much varied
information as possible within each dataset and
meanwhile be correlated as highly as possible
across data sets. Some other significant attribute
of these subLV’s is that the subLV’s within each
dataset were interrelated with each other.
Nevertheless, multi latent variable may not be
capable to totally recover the underlying sources.
So, finally, IVA was performed to these extracted
sub-LVs and helped to attain the end of the JBSS
technique. The recovered sources are shown in
Figure 7, from this to understand that the roots of
each dataset have been accurately recovered and
the corresponding sources are highly correlated
across data sets. Mention that the EEEMD-IVA
method recovered all underlying sources of the
unidimensional dataset X3. Likewise, this method
is used in real time mixed both Uni and multi
dimensional EEG signals. Table 2 shows the
comparison results of existing and proposed
algorithm for various metrics.
Table 2. Comparison results
Metric
Separ
ated
signal
STFT
ICA
Wavelet
ICA
IVA
EEMD-
IVA
Propose
d
Algorith
m
ACC(Ge
nerated
Signals)
X1 0.5516 0.5902 0.6829 0.8239 0.9147
X2 0.7204 0.7236 0.7928 0.8569 0.8949
X3 0.5824 0.7623 0.7922 0.8421 0.9021
ACC(EE
G
Signals)
X1 0.4892 0.5896 0.7346 0.8123 0.8721
X2 0.3756 0.4832 0.5928 0.8062 0.8635
X3 0.2267 0.4548 0.6321 0.8259 0.8894
SNR
Value
for
Generate
d Signals
X1 28.2016 28.9509 32.3696 39.6702 45.5391
X2 34.4792 34.6353 35.4191 41.0856 44.9852
X3 28.4946 35.0236 35.4054 40.2754 45.2116
SNR
Value of
EEG
Signals
X1 27.9894 28.8772 34.8421 38.4731 43.1427
X2 23.7251 27.7832 29.5642 37.9638 42.8631
X3 20.2414 27.1214 31.9894 39.0381 43.5491
CONCLUSION
EEG signals can be applied effectively to examine
the mental states and ailments related to the mind.
The inherent issues with the EEG signal are that it
is highly nonlinear and non Gaussian in nature
and its visual interpretations are tedious and
subjective prone to inter observer variations. To
help researchers better analyze EEG signals, in
this research presented various signal analysis
techniques such as linear, frequency, time–
frequency domain methods. Compared to all other
methods EEEMD-IVA is working better it is
handled both multi and unidimensional brain
signal (EEG) concurrently, the average correlation
coefficient and SNR values are higher compare to
other methods.
REFERENCES
1. Zhang Chaozhu, Lian Siyao, Ahmed
Kareem Abdullah, A New Blind Source
Separation Method to Remove Artifact in
EEG Signals, 2013 Third International
Conference on Instrumentation,
Measurement, Computer, Communication
and Control.
2. N. Abdolmaleki and M. Pooyan, Source
Separation From Single Channel
Biomedical Signal By Combination Of
Blind Source Separation And Empirical
Mode Decomposition, International
Journal of Digital Information and
Wireless Communications (IJDIWC) 2(1):
75-81 The Society of Digital Information
and Wireless Communications, 2012
(ISSN 2225-658X).
3. Bogdan Mijovi´c, Maarten De Vos, Ivan
Gligorijevi´c, Joachim Taelman, Sabine
Van Huffel, Source Separation From
Single-Channel Recordings by Combining
Empirical-Mode Decomposition and
Independent Component Analysis, IEEE
TRANSACTIONS ON BIOMEDICAL
ENGINEERING, VOL. 57, NO. 9,
SEPTEMBER 2010.
4. Aapo Hyvärinen, Erkki Oja, Independent
Component Analysis: Algorithms and
Applications, Neural Networks, 13(4-5),
pp. 411-430, 2000.
5. Aapo Hyvärinen, Fast and Robust Fixed-
Point Algorithms for Independent
Component Analysis, IEEE Trans. Neural
Networks,10(3), pp. 626-634, 1999.
6. Bela Zhang, Liping Li, Blind Noncircular
Source Separation in Frequency Domain,
2010 International Symposium on
Intelligent Signal Processing and
Communication Systems (lSPACS 2010)
December 6-8,2010.
7. Ruhi Mahajan, Bashir I. Morshed,
Unsupervised Eye Blink Artifact
Denoising of EEG Data with Modified
Multiscale Sample Entropy, Kurtosis and
Wavelet-ICA, DOI
10.1109/JBHI.2014.2333010, IEEE
Journal of Biomedical and Health
Informatics
AJCSE,
Nov-Dec,
2017,
Vol.
2,
Issue
6

8. Patrizio Campisi, Daria La Rocca, Brain
Waves for Automatic Biometric-Based
User Recognition, IEEE
TRANSACTIONS ON INFORMATION
FORENSICS AND SECURITY, VOL. 9,
NO. 5, MAY 2014.
9. T.-P. Jung, S. Makeig, M.J. McKeown,
A.J. Bell, T.-W. Lee, T.J. Sejnowski,
Imaging brain dynamics using independent
component analysis, Proc IEEE 89 (7)
(2001) 1107–1122.
10. Yongchao Yang, S.M.ASCE, and Satish
Nagarajaiah, Time-Frequency Blind
Source Separation Using Independent
Component Analysis for Output-Only
Modal Identification of Highly Damped
Structures, JOURNAL OF
STRUCTURAL ENGINEERING ©
ASCE / OCTOBER 2013, 139:1780-1793.
11. Salvatore Calcagno, Fabio La Foresta and
Mario Versaci, Independent Component
Analysis And Discrete Wavelet Transform
For Artifact Removal In Biomedical
Signal Processing, American Journal of
Applied Sciences 11 (1): 57-68, 2014.
12. Intae Lee, Taesu Kim, Te-Won Lee, Fast
fixed-point independent vector analysis
algorithms for convolutive blind source
separation, Elsevier, Signal Processing 87
(2007) 1859–1871.
13. Jushan BAI, Serena NG, Tests for
Skewness, Kurtosis, and Normality for
Time Series Data, 2005 American
Statistical Association, Journal of Business
& Economic Statistics January 2005, Vol.
23, No. 1DOI
10.1198/073500104000000271.
14. Xun Chen, Aiping Liu, Martin J.
McKeown, Howard Poizner, and Z. Jane
Wang, An EEMD-IVA Framework for
Concurrent Multidimensional EEG and
Unidimensional Kinematic Data Analysis,
IEEE TRANSACTIONS ON
BIOMEDICAL ENGINEERING, VOL.
61, NO. 7, JULY 2014.
15. T. Kim, T. Eltoft, and T. W. Lee,
“Independent vector analysis: An
extension of ICA to multivariate
components,” in Proc. Independent
Component Analysis, Blind Signal
Separation, 2006, pp. 165–172.
16. M. Anderson, T. Adali, and X. L. Li,
“Joint blind source separation with
multivariate Gaussian model: Algorithms
and performance analysis,” IEEE Trans.
Signal Process., vol. 60, no. 4, pp. 1672–
1683, Apr.2012.
17. N. E. Huang, Z. Shen, S. R. Long, M. C.
Wu, H. H. Shin, Q. Zheng, N. C. Yen, C.
C. Tung, and H. H. Liu, “The empirical
mode decomposition and the Hilbert
spectrum for nonlinear and nonstationary
time series analysis,” Proc. Roy. Soc. A
Math. Phys. Eng. Sci., vol. 454, no. 1971,
pp. 903–995, 1998.
18. B. Mijovic, M. D. Vos, I. Gligorijevic, J.
Taelman, and S. V. Huffel, “Source
separation from single-channel recordings
by combining empirical-mode
decomposition and independent
component analysis,” IEEE Trans.
Biomed. Eng., vol. 57, no. 9, pp. 2188–
2196, Sep. 2010.
19. M. Hassan, S. Boudaoud, J. Terrien, B.
Karlsson, and C. Marque, “Combination of
canonical correlation analysis and
empirical mode decomposition applied to
diagnosing the labor electrohysterogram,”
IEEE Trans. Biomed. Eng., vol. 58, no. 9,
pp. 2441–2447, Sep. 2011.
20. K. T. Sweeney, S. F. McLoone, and T. E.
Ward, “The use of ensemble empirical
mode decomposition with canonical
correlation analysis as a novel artifact
removal technique,” IEEE Trans. Biomed.
Eng., vol. 60, no. 1, pp. 97–105, Jan. 2013.
21. Z.Wu and N. E. Huang, “Ensemble
empirical mode decomposition: A noise-
assisted data analysis method,” Adv.
Adapt. Data Anal., vol. 1, no.1,pp.1–
41,2009.
AJCSE,
Nov-Dec,
2017,
Vol.
2,
Issue
6

Improved Algorithm for Brain Signal Analysis

More Related Content

Similar to Improved Algorithm for Brain Signal Analysis (20)

More from BRNSSPublicationHubI (20)

Recently uploaded (20)

Improved Algorithm for Brain Signal Analysis