Electrocerebral non-linear dual-measure feature extraction and fusion processing method

A technology of feature extraction and fusion processing, applied in the field of EEG signals, can solve the problems of lack of specific physiological indicators and achieve high classification accuracy

Inactive Publication Date: 2014-05-07
TIANJIN UNIV
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Problems solved by technology

[0003] At present, the evaluation of a person's mental and psychological state mainly relies on some psychological scales (self-evaluation scale or other evaluation scale). According to the score of the scale, the mental and psychological state of the person is evaluated, and there is a lack of specific physiological conditions. indicators for evaluation

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  • Electrocerebral non-linear dual-measure feature extraction and fusion processing method

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[0024] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0025] In order to perform two types of nonlinear feature extraction and fusion processing on the collected EEG signals, an embodiment of the present invention provides an EEG nonlinear dual-measure feature extraction and fusion processing method, as described below for details:

[0026] The nonlinear analysis method focuses on analyzing the dynamic characteristics of brain electrical activity over time. It has been applied to the research of various neuropsychiatric diseases such as Alzheimer's disease, schizophrenia, and depression, and has achieved corresponding results. Research results. LZC (Lempel-Ziv Complexity, LZC) complexity is a nonlinear measurement method that characterizes the degree of disorder by measuring the rate at w...

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Abstract

The invention discloses an electrocerebral non-linear dual-measure feature extraction and fusion processing method, and relates to the field of electrocerebral signals. The method comprises the following steps: acquiring an electrocerebral signal; performing data preprocessing on the electrocerebral signal; extracting an LZC (Leucocyte Zinc Content) complexity feature from preprocessed data; extracting a sample entropy feature, and performing feature analysis on the LZC complexity feature and the sample entropy feature; establishing a classification model with an SVM (Support Vector Machine) classifier by jointly taking the LZC value of the electrocerebral signal and a sample entropy value which are remarkably different as a feature parameter in order to classify and identify people in different psychological states. By adopting the method for classifying and identifying the electrocerebral signals of people in different psychological states, higher classifying accuracy can be achieved, and an objective evaluation index can be provided for the partition of people in different psychological states.

Description

technical field [0001] The invention relates to the field of EEG signals, in particular to an EEG nonlinear double-measure feature extraction and fusion processing method. Background technique [0002] With the rapid development of society, people's living environment, lifestyle and habits, and working conditions have all changed. The increasingly fast pace of life makes people in modern cities feel more and more lonely and depressed. Facing such a social environment, Abnormal mental and psychological conditions have become an unavoidable health problem for the whole society. Due to the improvement of material life, people pay more and more attention to mental health, how to detect the abnormality of mental and psychological state early has become an urgent problem to be solved. [0003] At present, the evaluation of a person's mental and psychological state mainly relies on some psychological scales (self-evaluation scale or other evaluation scale). According to the score ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0476
Inventor 明东王春方张力新张希何峰綦宏志赵欣周鹏万柏坤
Owner TIANJIN UNIV
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