Elimination method for myoelectricity artifacts in small-number-channel brain electrical signals

An EEG signal and channel technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve the problem of inability to completely remove EMG artifacts and only consider them, so as to achieve the effect of removing EMG artifacts and reducing loss.

Active Publication Date: 2017-06-09
HEFEI UNIV OF TECH
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Problems solved by technology

However, these single-channel processing methods ignore the correlation information between channels and only consider the information in a single channel, which leads to the inability to completely remove myoelectric artifacts.

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  • Elimination method for myoelectricity artifacts in small-number-channel brain electrical signals
  • Elimination method for myoelectricity artifacts in small-number-channel brain electrical signals
  • Elimination method for myoelectricity artifacts in small-number-channel brain electrical signals

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specific Embodiment approach

[0040] Step 1: Collect and record the EEG signals of N channels at time t by the EEG measurement equipment, which is recorded as: X(t)=[x 1 (t), x 2 (t),...,x n (t),...x N (t)] T , x n (t) is the EEG signal of the nth channel at time t, and T is the transposition of the matrix; 1≤n≤N, N≥3;

[0041] In the present embodiment, the EEG signal of 3 channels is collected and recorded by the EEG measuring equipment at time t, which is denoted as: X(t)=[x 1 (t),x 2 (t),x 3 (t)] T . Among them, X EEG (t)( Figure 2a ) and X EMG (t)( Figure 2b ) represent the real clean EEG signal and myoelectric artifact signal of 3 channels respectively, X(t)=X EEG (t)+λX EMG (t)( Figure 2c , λ is 41.6733 calculated from the signal-to-noise ratio SNR=1.5), where X EEG (t)=[x EEG1 (t),x EEG2 (t),x EEG3 (t)] T , X EMG (t)=[x EMG1 (t),x EMG2 (t),x EMG3 (t)] T .

[0042] Signal-to-noise ratio definition: SNR=RMS(X EEG ) / RMS(λ·X EMG ), where RMS means root mean square, with...

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Abstract

The invention discloses an elimination method for myoelectricity artifacts in small-number-channel brain electrical signals. The elimination method includes the steps that 1, the small-number-channel brain electrical signals are decomposed at the same time with multi-element experience empirical mode decomposition, and a small-number-channel intrinsic mode component matrix is obtained; 2, the small-number-channel intrinsic mode component matrix is subjected to blind signal separation through independent variable analysis; 3, components with the myoelectricity artifacts are judged with autocorrelation coefficients, myoelectricity artifact components are subjected to zero setting, and a component matrix without the myoelectricity artifacts is obtained through independent variable analysis inverse transformation; 4, according to the marshalling sequence of the original intrinsic mode component matrix, intrinsic mode components of corresponding channels are sequentially added, and clean brain electrical signals are finally obtained. By means of the elimination method, the influence of the myoelectricity artifacts on the brain electrical signals can be completely removed, and therefore the analysis accuracy of the brain electrical signals is improved.

Description

technical field [0001] The invention belongs to the technical field of EEG signal processing, and specifically relates to a new method for automatically identifying and eliminating EMG artifacts from a few channels of EEG signals based on multiple empirical mode decomposition and independent vector analysis, and is mainly used in human brain related The study of disease and human brain function. Background technique [0002] As a device for recording the electrophysiological activity of brain nerve cells, EEG has been widely used in clinical disease diagnosis, brain special diagnosis and sleep mode due to its high temporal resolution, portability and non-invasiveness. etc. research. However, as a weak electrophysiological signal, EEG signals are often interfered by various artifacts such as ECG, EEG, and EMG, which affect the accuracy of subsequent EEG analysis. In addition, due to the characteristics of large amplitude, wide frequency distribution and complex geographical...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00
CPCA61B5/7203
Inventor 陈勋徐雪远陈强成娟刘羽
Owner HEFEI UNIV OF TECH
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