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EEG signal denoising method based on eemd and dss-apen

An electroencephalogram signal and denoising technology, which is applied in the field of signal processing, can solve the problems such as the inability to directly determine the effective signal of the independent source component, the uncertainty of the sequence of the independent source component, etc., and achieve the effect of broad application prospects.

Active Publication Date: 2021-02-19
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, the order of each independent source component in the DSS result is uncertain, and it cannot be directly determined whether the independent source component is a noise signal or a valid signal

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  • EEG signal denoising method based on eemd and dss-apen
  • EEG signal denoising method based on eemd and dss-apen
  • EEG signal denoising method based on eemd and dss-apen

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Embodiment Construction

[0022]The following describes the denoising method for EEG signals based on EEMD and DSS-ApEn of the present invention in conjunction with the accompanying drawings.

[0023]figure 1 For the denoising process of EEG signals, its implementation mainly includes the following steps:

[0024](1) Use EEMD to decompose the signal to be denoised into IMF sets, and remove the highest frequency IMF components to obtain a new IMF set X.

[0025]Use EEMD to decompose the signal to be denoised into IMF set, as follows:

[0026]Step 1. Add white noise n with a mean value of 0 and a constant standard deviation to the signal s(t) to be processed1(t), get the signal s to be decomposed1(t).

[0027]Step 2. EMD algorithm decomposes signal s1(t), get n IMF components imfi1(t) and remainder rn1(t), namely:

[0028]

[0029]Among them, n is the number of decomposition layers.

[0030]Step 3. Repeat the above two steps T-1 times, but add different white noise of the same distribution each time.

[0031]Step 4. Perform overall aver...

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Abstract

The present invention proposes an EEG signal denoising method based on EEMD and DSS-ApEn. Use the EEMD decomposition algorithm to decompose the noisy EEG signal into several intrinsic mode function IMF (Intrinsic Mode Functions) components, and apply DSS to separate the independent source signals from the IMF components after filtering out the highest frequency components, and then select the spectral approximation The independent source signal with the largest entropy is used as the denoised signal. The waveform of the EEG signal after denoising by the denoising method proposed by the present invention is relatively clear, and more importantly, the details of the original signal are well preserved.

Description

Technical field[0001]The invention belongs to the field of signal processing, and relates to a method for processing brain electrical signal noise, in particular to a method for eliminating brain electrical signal noise for motor imagination.Background technique[0002]Scalp EEG (Electroencephalogram, EEG) has an irreplaceable role in brain-computer interface applications that have been widely concerned recently due to its non-invasiveness, easy acquisition and good time resolution. However, because the scalp EEG is a non-stationary, nonlinear and extremely weak random signal, it is easily overwhelmed by a large number of external interference signals such as ECG, eye electricity, electromagnetic interference, power frequency interference, etc., so noise cancellation becomes EEG signal prediction. One of the most important steps in the processing phase.[0003]Empirical Mode Decomposition (EMD) is an adaptive data decomposition method that can handle random non-stationary signals well, ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/372
CPCA61B5/7203A61B5/369
Inventor 孟明杨国雨佘青山马玉良罗志增
Owner HANGZHOU DIANZI UNIV
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