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Electroencephalogram signal fatigue feature extraction method based on common spatial mode fuzzy wavelet packet

A co-space mode and EEG signal technology, applied in the direction of mechanical mode conversion, character and pattern recognition, electrical digital data processing, etc., can solve problems such as difficult classification, achieve high fatigue recognition accuracy, solve fatigue detection rate low, The effect of resolving classification difficulties

Pending Publication Date: 2022-02-15
JIANGSU UNIV OF SCI & TECH
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Problems solved by technology

[0005] In order to solve the above-mentioned technical problems, the present invention provides a method for extracting fatigue features of EEG signals based on co-space pattern fuzzy wavelet packets that effectively solves the difficult problem of classification and improves the accuracy of fatigue detection.

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  • Electroencephalogram signal fatigue feature extraction method based on common spatial mode fuzzy wavelet packet
  • Electroencephalogram signal fatigue feature extraction method based on common spatial mode fuzzy wavelet packet
  • Electroencephalogram signal fatigue feature extraction method based on common spatial mode fuzzy wavelet packet

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Embodiment Construction

[0027] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0028] Please refer to figure 1 , figure 2 , image 3 with Figure 4 ,in, figure 1 Schematic diagram of placement of EEG signal acquisition electrodes of the international standard 10-20 electrode system provided by the present invention; figure 2 CSPFWPT EEG signal fatigue feature extraction method flow chart provided by the present invention; image 3 A flow chart of the different coefficient M feature extraction methods of the CSP method provided by the present invention; Figure 4 The frequency decomposition tree diagram of the wavelet packet EEG signal provided by the present invention. The fatigue feature extraction method of EEG signal based on co-space pattern fuzzy wavelet packet includes the following steps:

[0029] S1. Construct the maximum energy information extraction method FEMI to fully extract the maximum energy information and...

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Abstract

The invention provides an electroencephalogram signal fatigue feature extraction method based on a common spatial mode fuzzy wavelet packet, and the method comprises the steps: S1, constructing a maximum energy information extraction method FEI, fully extracting maximum energy information, and reducing redundant information; S2, extracting an electroencephalogram data set decomposed by the WPT by using the FEI, and obtaining an FWPT feature set; S3, changing a CSP output selection coefficient M from 1 to 5, and selecting a multi-scale feature information set; S4, carrying out optimization on the obtained five CSP feature sets, and fully utilizing the fatigue information amount; S5, carrying out the multi-feature fusion, and acquiring the CSPFWPT features. The electroencephalogram signal fatigue feature extraction method based on the common space mode fuzzy wavelet packet has the advantages that the problem of classification difficulty is effectively solved, and the fatigue detection accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of fatigue detection of biological signal processing, in particular to a method for extracting fatigue features of brain electrical signals based on co-space pattern fuzzy wavelet packets. Background technique [0002] Electroencephalogram signal (EEG) is a typical bioelectrical signal, which is the overall reflection of the electrical activity of cerebral cortex brain nerve cells, which contains a large number of physiological and pathological signals. EEG is the spontaneous activity of neuron cells in the human central nervous system. Sexual and rhythmic electrophysiological activities. EEG signals are the overall response of the synchronous activities of a large number of neuron cell groups on the surface of the cerebral cortex and scalp. They can be recorded by implantation or external electrodes. In recent years, the detection of EEG signals has been considered It is an important and reliable method of ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06F3/01
CPCG06F3/015G06F2218/08G06F18/2411
Inventor 郑威刘燕
Owner JIANGSU UNIV OF SCI & TECH