Electroencephalogram signal denoising method based on self-adaption threshold processing

An adaptive threshold and EEG signal technology, applied in the fields of application, medical science, sensors, etc., to achieve the effect of reducing the root mean square error and improving the signal-to-noise ratio

Active Publication Date: 2014-08-06
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of removing EEG signal noise by traditional thr

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  • Electroencephalogram signal denoising method based on self-adaption threshold processing
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  • Electroencephalogram signal denoising method based on self-adaption threshold processing

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Embodiment Construction

[0015] The present invention will be further described below in conjunction with accompanying drawing.

[0016] The present invention comprises the following steps:

[0017] Step 1. Select the appropriate wavelet basis function, confirm the decomposition level j, and decompose the noisy EEG signal to the j level, and obtain the corresponding wavelet decomposition coefficient w j,k .

[0018] Step 2. Calculate the Donoho threshold of each decomposed subspace, which is the key to the wavelet threshold processing algorithm. It is necessary to select a threshold function and an appropriate threshold for denoising.

[0019] Step 3. Reconstruct the low-frequency coefficients and the processed high-frequency coefficients to obtain the EEG signal after denoising.

[0020] The specific steps for determining the wavelet decomposition level j in step 1 are as follows:

[0021] (1) The wavelet basis function db4 is selected to decompose the original EEG signal in 2 and 3 layers respect...

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Abstract

The invention relates to an electroencephalogram signal denoising method based on self-adaption threshold processing. The method comprises the following steps: first improving a threshold function on the basis of a soft threshold; second, conducting multi-layer decomposition on an acquired electroencephalogram signal, and obtaining a corresponding wavelet detail coefficient; then, improving the threshold according to the statistical correlation of the wavelet coefficient after wavelet decomposition, conducting self-adaption threshold processing on the wavelet coefficient; finally, reconstructing the wavelet coefficient after zooming to obtain a denoised EEG signal. Compared with a hard threshold method, a soft threshold method and a Garrote threshold method, the electroencephalogram signal denoising method has the advantages that smoothness of the soft threshold method is maintained, the Gibbs phenomenon is reduced, gaussian noise is effectively suppressed, most usable detail information in an EEG is reserved, and a good foundation is laid for EEG characteristic extraction and mode identification in the next step.

Description

technical field [0001] The invention relates to a method for preprocessing electroencephalogram signals, in particular to a method for denoising electroencephalogram signals based on an improved threshold algorithm. Background technique [0002] The brain is a complex system composed of hundreds of millions of neurons, which are responsible for the coordinated operation of various functions of the human body. The potential activity of brain cell groups recorded through electrodes on the cerebral cortex is called Electroencephalogram (EEG). Through the analysis and research of EEG, a wealth of physiological, psychological and pathological information can be obtained, and it is an important tool in the field of clinical medicine and brain research. However, EEG is a nonlinear and non-stationary signal with strong randomness, and the signal strength is very weak. In the process of acquisition and processing, it is very vulnerable to various noises and artifacts such as electroc...

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Application Information

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IPC IPC(8): A61B5/0476
Inventor 马玉良许明珍张启忠高云园孟明佘青山
Owner HANGZHOU DIANZI UNIV
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