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ISBN 978-952-5726-09-1 (Print)
Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10)
Jinggangshan, P. R. China, 2-4, April. 2010, pp. 242-245
Study of Electromyography Based on Informax
ICA and BP Neural Network
Guangying Yang, and Shanxiao Yang
School of Physical & Electronics Engineering, Taizhou University, Taizhou City, China
ygy@tzc.edu.cn
Abstract— Surface electromyography (SEMG) signals
decomposition algorithm based on Independent Component
Analysis (ICA) are explored. The experiment shows that
this method can decompose SEMG signal efficiently on the
premise that different motor units are all independent. Then
it can be concluded that ICA is a promising method of
preprocessing for SEMG decomposition. After SEMG has
been reconstructed, we create AR model with the original
signal that was pretreated and take the coefficient as its
eigenvector. Then, a three-layer BP neural network was
designed to classify the muscle movement of forearm with
AR model coefficient. The experiment indicates this
measure can reduce workload and get the relatively good
results.
Index Terms—Independent Component Analysis (ICA);
Surface Electromyography Signal; BP Neural Network;
Pattern Recognition
I. INTRODUCTION
Independent component analysis (ICA) is a way of
finding a linear nonorthogonal coordinate system in any
multivariate data. It is a new technique to separate blind
sources, which has been used in some challenging fields
of EMG, ECG, EEG processing. The directions of the
axes of this coordinate system are determined by
maximizing the statistical independence of the estimated
components. Under a certain condition, we can separate
independent part from source signal [1].
Electromyogram (EMG) is a signal obtained by
measuring the electrical activity in a muscle has been
widely used both in clinical practice and in the
rehabilitation field [2]. Clinical analysis of the EMG is a
powerful tool used to assist the diagnosis of
neuromuscular disorders [3]. BP neural network (BPNN)
is backpropagation algorithm in the medical field for the
development of decision support systems [4]. In this paper,
we discuss EMG signal by adopting the Informax ICA
calculating way which has better effect to resolve the
surface muscle telecommunication signal. And it can be a
preprocessing mean to decompose the surface
telecommunication signal.
II.
RECOGNITION METHODS OF MUSCLE MOTION
A. Auto-Regression(AR) Model
This work is supported by education department Program of
Zhejiang Province in University (2010) and Yong teacher Program of
Taizhou University (09qn09)..
Parametric model is an important method for analysis
of electromyography signal, where the most typical is AR
Mode. According to the AR model’s theoretical analysis,
we could see that the parameter selection is critical.
Proper parameters are conductive to the recognition and
parametrical evaluation of AR model. This paper adopts
one of the evaluation way is direct evaluation which
derives the model parameters directly from observed data
or statistic characteristics of the data [5]. The recognition
of mode [6] evaluates the model’s parameters with a
stable model structure and degrees by the means of auto
covariance function and partial correlation function’s
character of truncation according to the information
implied in a sample derived from the time sequence.
The stationary AR (p) model is stable if the roots
λk(a), k = 1 . . . . . p, of the associated characteristic
polynomial have moduli that are less than unity.
p
k
a( z)
= 1 −∑ ak
z − , z∈C
(1)
k = 1
We define the stationary AR model as stable with
margin 1 - p if all roots of the model's characteristic
polynomial a (z) lie inside a circle of radius p < 1 in the
complex plane. Correspondingly, a time-varying AR (p)
model is stable if the roots of the corresponding timevarying
characteristic polynomial.
p
k
a( z; t)
= 1 −∑ ak
( t) z − , z∈C
(2)
k = 1
We define an AR model as hyperstabte if all roots of
the model's characteristic polynomial lie inside a circle of
radius p _< 1 in the complex plane. In this paper we
present a method for estimating hyperstable AR models.
Although the proposed method is applicable to other
tranversal AR parameter estimation schemes, we discuss
here only the nonwindowed least squares (LS) method.
The choice of model order p poses great problems.
According to the past research and experiment [6], the
experiment sets AR model to four because higher value
of the order will not improve the performance but also
will add burden of computation.
B. Informax ICA Algorithm
The idea behind using the Independent Component
Analysis (ICA) is to reduce the redundancy of the original
feature vector components. Assume that there is an N-
dimensional zero-mean non-Gaussian source vector
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