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Noise Estimation in Electroencephalogram Signal by Using Volterra Series Coefficients.

Hassani M, Karami MR - J Med Signals Sens (2015 Jul-Sep)

Bottom Line: An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased.Hence, in many applications it is urgent to reduce the complexity of computation.The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran.

ABSTRACT
The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

No MeSH data available.


Related in: MedlinePlus

Histogram of estimated noise when P = 250 and variance of noise is 0.3
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Figure 8: Histogram of estimated noise when P = 250 and variance of noise is 0.3

Mentions: Furthermore, in Figure 8 we depicted the histogram of estimated noise when P = 250 and variance of noise is 0.3, and then we calculated the goodness of fit by applying Chi-square test in different variances of noise and P = 250 for level significant of 0.05. As we know the P > 0.05 means, there is no significant difference between the estimated noise and the existing noise in the signal. We performed the simulation several times and Table 8 shows the percentage of times that the P value of Chi-square test is >0.05 in different variances of noise, that is, denoted by G. We can see, in most of the time the P > 0.05 and also G is increased by increasing variance of noise.


Noise Estimation in Electroencephalogram Signal by Using Volterra Series Coefficients.

Hassani M, Karami MR - J Med Signals Sens (2015 Jul-Sep)

Histogram of estimated noise when P = 250 and variance of noise is 0.3
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528358&req=5

Figure 8: Histogram of estimated noise when P = 250 and variance of noise is 0.3
Mentions: Furthermore, in Figure 8 we depicted the histogram of estimated noise when P = 250 and variance of noise is 0.3, and then we calculated the goodness of fit by applying Chi-square test in different variances of noise and P = 250 for level significant of 0.05. As we know the P > 0.05 means, there is no significant difference between the estimated noise and the existing noise in the signal. We performed the simulation several times and Table 8 shows the percentage of times that the P value of Chi-square test is >0.05 in different variances of noise, that is, denoted by G. We can see, in most of the time the P > 0.05 and also G is increased by increasing variance of noise.

Bottom Line: An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased.Hence, in many applications it is urgent to reduce the complexity of computation.The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran.

ABSTRACT
The Volterra model is widely used for nonlinearity identification in practical applications. In this paper, we employed Volterra model to find the nonlinearity relation between electroencephalogram (EEG) signal and the noise that is a novel approach to estimate noise in EEG signal. We show that by employing this method. We can considerably improve the signal to noise ratio by the ratio of at least 1.54. An important issue in implementing Volterra model is its computation complexity, especially when the degree of nonlinearity is increased. Hence, in many applications it is urgent to reduce the complexity of computation. In this paper, we use the property of EEG signal and propose a new and good approximation of delayed input signal to its adjacent samples in order to reduce the computation of finding Volterra series coefficients. The computation complexity is reduced by the ratio of at least 1/3 when the filter memory is 3.

No MeSH data available.


Related in: MedlinePlus