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EMD and gaussian kernel function SVM-based EEG emotion classification method

A Gaussian kernel function and emotion classification technology, applied in the field of EEG emotion signal pattern recognition, can solve the problem of low accuracy

Inactive Publication Date: 2017-10-20
BEIJING UNIV OF TECH
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Problems solved by technology

[0005] At the present stage, there are many methods of using classification algorithms to identify and classify emotional EEG, but there is a problem of low accuracy. How to improve the accuracy of classification has always been a difficult problem to solve.

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  • EMD and gaussian kernel function SVM-based EEG emotion classification method
  • EMD and gaussian kernel function SVM-based EEG emotion classification method
  • EMD and gaussian kernel function SVM-based EEG emotion classification method

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Embodiment Construction

[0030] The experiment adopts the online public data set DEAP database, and the extracted experimental data is divided into two parts, one part is the EEG signal data of the subjects, and the other part is the emotional self-evaluation data of the subjects for the MV. The processing process of the EEG data is to remove the EEG signal interference in the EEG signal by filtering method firstly, and then reduce the EEG sampling frequency to 128HZ by down-sampling method. Correlation analysis is mainly carried out on the EEG signal in the frequency domain, and the brain activity characteristics of people in the waking state are mainly concentrated in the bands with relatively fast frequency and relatively concentrated energy. Therefore, the EMD method is used to decompose the original EEG signal obtained initially into 12 Intrinsic Mode Functions (IMFs), and then extract the energy features of the IMFs in the frequency domain as feature values ​​for subsequent sentiment classificati...

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Abstract

The invention discloses an EMD and gaussian kernel function SVM-based EEG emotion classification method. Aiming at the problem that the accuracy of EEG signal classification is not high, an Empirical Mode Decomposition (EMD) technology and SVMs are combined, first EMD is performed on an EEG signal to obtain a plurality of modal components, each modal component contains effective information of different frequencies, then frequency energy is used as a quantitative criterion of each modal component, i.e., each EEG signal can obtain different characteristic values, and the characteristic values are used as characteristic values of an EEG sequence to perform a next step of sample value classification. Experiments show that the EMD and gaussian kernel function SVM-based EEG classification method can improve the accuracy of EEG signal classification.

Description

technical field [0001] The invention relates to the field of EEG emotion signal pattern recognition, and is suitable for the application of classifying different types of EEG signals. Background technique [0002] As a high-level function of the human brain, emotion ensures people's daily survival and adaptation to the objective environment, and affects people's study, work and decision-making to varying degrees. [0003] The identification of people's emotional state and the judgment of changing trends have important and broad application prospects in the fields of education, medical care, entertainment, and business. [0004] The expression of people's emotions mainly includes inner experience and external behavior, and is accompanied by complex neural processes and physiological changes. For the performance of people's emotional changes, in addition to observing the relative changes in people's expressions, intonation and body posture, it can also be expressed as changes...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/12G06F18/23213G06F18/2411
Inventor 李幼军钟宁陈萌刘岩何强
Owner BEIJING UNIV OF TECH
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