EEMD (Ensemble Empirical Mode Decomposition) and wavelet threshold based motor imagery electroencephalogram signal denoising method

A technology of motor imagery and EEG signals, which is applied in the direction of electrical digital data processing, input/output process of data processing, input/output of user/computer interaction, etc. The effect of reducing root mean square error and reducing RMSE

A technology of motor imagery and EEG signals, which is applied in the direction of electrical digital data processing, input/output process of data processing, input/output of user/computer interaction, etc. The effect of reducing root mean square error and reducing RMSE

CN105677035AActive Publication Date: 2016-06-15西安慧脑智能科技有限公司
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  • EEMD (Ensemble Empirical Mode Decomposition) and wavelet threshold based motor imagery electroencephalogram signal denoising method
  • EEMD (Ensemble Empirical Mode Decomposition) and wavelet threshold based motor imagery electroencephalogram signal denoising method
  • EEMD (Ensemble Empirical Mode Decomposition) and wavelet threshold based motor imagery electroencephalogram signal denoising method

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Embodiment Construction

[0032] The present invention will be further described below in conjunction with accompanying drawing.

[0033] The present invention comprises the following steps:

[0034] Step 1. Select the added noise number M and the added white noise sequence amplitude coefficient k, and perform EEMD decomposition on the original motor imagery EEG signal to obtain a series of intrinsic mode function IMF components from high to low;

[0035] Step 2. select the threshold function and the threshold to carry out denoising processing to the first few high-frequency IMF components containing noise;

[0036] Step 3. Reconstruct the IMF component after wavelet threshold denoising and other IMF components to obtain the motor imagery EEG signal after denoising.

[0037] The specific steps of EEMD decomposition in step 1 are as follows:

[0038] (1) Add white noise with zero mean and constant standard deviation to the original signal, and repeat this step M times. The value of M is determined by...

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Abstract

The invention relates to an EEMD (Ensemble Empirical Mode Decomposition) and wavelet threshold based motor imagery electroencephalogram signal denoising method. The method comprises the steps of firstly, performing EEMD on an original signal to obtain a series of IMF (Intrinsic Mode Function) components; secondly, improving a conventional wavelet threshold method with a new threshold function and a threshold selection method; thirdly, processing a high-frequency IMF component with the improved wavelet threshold method; and finally, reconstructing the processed IMF component and other IMF components to obtain a denoised motor imagery EEG (electroencephalogram) signal. The method has the advantages that effective information in the high-frequency component is reserved, the suppression of the wavelet threshold method to a weak-energy effective signal is reduced, most of useful detailed information is reserved while a large amount of noises are eliminated, and a good foundation is laid for motor imagery EEG signal feature extraction and mode identification in the next step.

Description

technical field [0001] The invention relates to a method for preprocessing motor imagery EEG signals, in particular to a method for denoising motor imagery EEG signals based on EEMD and a wavelet threshold algorithm. Background technique [0002] Motor imagery EEG signals are often used in brain-computer interface systems in recent years. Due to its obvious non-stationarity and nonlinearity, and the signal amplitude is very weak, only 5-150 microvolts, and the frequency is as low as tens of hertz, it is very easy to be disturbed by ocular electricity, electrocardiogram, power frequency interference, electromagnetic interference, etc. The external interference signals are submerged, which brings great challenges to the research of this type of brain-computer interface system. Therefore, effective signal denoising processing is very important in the research. [0003] Wavelet analysis is often used to deal with random signals because of its multi-resolution and good time-fre...

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Application Information

Patent Timeline
15 Jun 2016
Publication
CN105677035A
IPC
G06F3/01; A61B5/0476
CPC
A61B5/7203; A61B5/369; G06F3/015
Inventors
马玉良; 蔡慧