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EEG signal denoising method based on EEMD and DSS-ApEn

An EEG signal and denoising technology, applied in the field of signal processing, can solve problems such as the inability to directly determine the effective signal of independent source components and the uncertain order of independent source components, and achieve the effect of broad application prospects

Active Publication Date: 2018-12-18
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 EEG signal denoising method based on EEMD and DSS-ApEn of the present invention will be described below with reference to the accompanying drawings.

[0023] figure 1 For the EEG signal denoising processing flow, its implementation mainly includes the following steps:

[0024] (1) Apply 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] Apply EEMD to decompose the signal to be denoised into IMF sets, as follows:

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

[0027] Step 2. The EMD algorithm decomposes the signal s 1 (t), get n IMF components imf i1 (t) and remainder r n1 (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 with ...

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Abstract

The invention provides an EEG signal denoising method based on EEMD and DSS-ApEn. The noisy EEG signal is decomposed into several intrinsic mode functions (IMF) components by using the EEMD decomposition algorithm; the IMF component with the highest frequency component filtered out is used to extract each independent source signal by DSS, and then the independent source signal with the largest approximate entropy of spectrum is selected as the denoising signal. The EEG signal has relatively clear EEG signal waveform after denoising by the denoising method provided in the invention, and more importantly, the detailed features of the original signal shelf are well preserved.

Description

technical field [0001] The invention belongs to the field of signal processing, and relates to an EEG signal noise processing method, in particular to a method for eliminating motor imagery EEG signal noise. Background technique [0002] Scalp EEG (Electroencephalogram, EEG) plays an irreplaceable role in the recently widely concerned brain-computer interface applications due to its non-invasiveness, easy acquisition and good time resolution. However, since 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 denoising becomes the EEG signal prediction. One of the most important steps in the processing phase. [0003] Empirical Mode Decomposition (EMD) is an adaptive data decomposition method, which can better deal with random non-stationary signals, and has good adaptability ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476
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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