Feature extraction of motion imaginary EEG signals based on weighted compound multi-scale fuzzy entropy

A technology of motor imagery and EEG signals, applied in character and pattern recognition, instruments, computer components, etc., can solve problems that are unreasonable and affect the accuracy of pattern classification, and achieve the effect of improving classification accuracy

Active Publication Date: 2019-01-18
BEIJING UNIV OF TECH
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Problems solved by technology

It is obviously unreasonable to use CMFE to deal with time-varying MI-EEG, which will inevitably affect the accuracy of pattern classification

Method used

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  • Feature extraction of motion imaginary EEG signals based on weighted compound multi-scale fuzzy entropy
  • Feature extraction of motion imaginary EEG signals based on weighted compound multi-scale fuzzy entropy
  • Feature extraction of motion imaginary EEG signals based on weighted compound multi-scale fuzzy entropy

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Embodiment Construction

[0037] The experimental data used in the present invention comes from the "BCI Competition 2003" competition database. The EEG signals of C3, Cz, and C4 were recorded with a sampling frequency of 128Hz and a band-pass filter of 0.5-30Hz. Acquisition timing diagram such as figure 1 As shown, a single experiment lasted 9s, and the subject was in a relaxed state for the first two seconds. At t=2s, a "+" cursor appeared on the screen. From t=3 to 9s, the subject completed imagining the movement of the left or right hand according to the prompts. . Both the training set and the test set contain 140 experimental data, among which, the left and right hands are imagined 70 times respectively.

[0038] Such as figure 2 Shown, the specific implementation steps of the present invention are:

[0039] (1) Preprocessing of EEG signals

[0040] For the MI-EEG signal imagining the movement of the left and right hands, under the category T motor imagery task, suppose is the i-th lead C...

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Abstract

The invention discloses a feature extraction method of motion imaginary EEG signal based on weighted compound multi-scale fuzzy entropy. At first, the composite multi-scale fuzzy entropy time sequenceof MI-EEG is calculated; According to the difference and variation of CMFE entropy in different leads of each motion imagination task T, the optimal time period is determined, and the MI-EEG signal is further used for feature extraction; Then, the weighting factors are introduced to obtain tao weighted coarse-grained sequences, and the fuzzy entropy of each coarse-grained sequence is calculated,and the average value of each coarse-grained sequence is defined as WCMFE; WCMFE at a single scale tao is computed for any lead Ci of various motion imagination tasks T; The variation range of scale factor tao is determined, WCMFE at multiple scales is computed, The eigenvectors (shown in the description) and the eigenvectors (FT) of all kinds of motion imagination tasks T are constructed and fused into MI-EEG feature vector F, further improving the classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of EEG signal processing, and is applied to feature extraction of motor imagery EEG signals in a Brain-Computer Interface (BCI) system, specifically using Weighted Composite Multiscale Fuzzy Entropy (Weighted Composite Multiscale Fuzzy Entropy, WCMFE) method for nonlinear dynamic feature extraction of motor imagery EEG signals. Background technique [0002] When people perform motor imagery, the cerebral cortex will generate motor imagery electroencephalography (MI-EEG) with rhythmic activity. Brain Computer Interface (BCI) technology based on motor imagery can be used in the training and rehabilitation of patients with motor nerve dysfunction. Since MI-EEG is sensitive to noise and has time-varying and fuzzy characteristics, its feature extraction has become a key issue in BCI-based rehabilitation engineering research. [0003] With the development of nonlinear dynamics, many studies have shown that the b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06F2218/08
Inventor 李明爱王若图杨金福
Owner BEIJING UNIV OF TECH
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