Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Electroencephalogram signal emotion recognition method and system based on VMD and WPD

An EEG signal and emotion recognition technology, applied in the field of emotion recognition, can solve the problem of feature extraction in the field of emotion recognition without application, and achieve the effect of large time-frequency resolution

Active Publication Date: 2020-06-19
SHANDONG NORMAL UNIV
View PDF7 Cites 9 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although these hybrid methods have been applied to the processing of EEG signals, they have not been applied to the feature extraction in the field of emotion recognition. Therefore, it is very necessary to propose a new feature extraction method based on hybrid time-frequency analysis.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Electroencephalogram signal emotion recognition method and system based on VMD and WPD
  • Electroencephalogram signal emotion recognition method and system based on VMD and WPD
  • Electroencephalogram signal emotion recognition method and system based on VMD and WPD

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0064] This disclosure firstly provides a multi-dimensional feature extraction method based on VMD and WPD, such as figure 1 Shown mainly includes the following processes:

[0065] Step S1: Obtain data from the emotional EEG data set, and preprocess the data;

[0066] Step S2: Perform VMD decomposition on the preprocessed EEG signal, and decompose to obtain VMF;

[0067] Step S3: Perform WPD on VMF to obtain VMF β+γ ;

[0068] Step S4: Extract VMF β+γ The WPE, MMSE, FD and 1ST form the emotional feature vector x;

[0069] Step S5: Send the feature vector x into the random forest classifier to identify the emotional state.

[0070] The multi-dimensional feature extraction algorithm based on VMD and WPD proposed in this disclosure can be described as:

[0071]

[0072]

[0073] In step S1 of this embodiment, this disclosure uses the internationally published multi-modal emotion data set DEAP, using the data of the MATLAB version that has been down-sampled and electro...

Embodiment 3

[0159] The present disclosure provides a VMD and WPD-based EEG signal emotion recognition system, including:

[0160] The variational mode decomposition module is configured to perform variational mode decomposition on the obtained EEG signal data to obtain a variational mode component;

[0161] The reconstruction module is configured to perform wavelet packet decomposition and reconstruction on the variational modal components to obtain reconstructed signals in the β and γ frequency bands in the EEG signal frequency band;

[0162] The feature vector composition module is configured to separately calculate the wavelet packet entropy of the reconstructed signal, the improved multi-scale sample entropy, the fractal dimension and the first-order difference value, and form the feature vector for the emotional recognition of the EEG signal;

[0163] The classification and recognition module is configured to send the feature vector into the classifier for classification and recognitio...

Embodiment 4

[0167] The present disclosure provides a computer-readable storage medium, which is characterized in that it is used to store computer instructions, and when the computer instructions are executed by a processor, the steps described in a VMD- and WPD-based EEG signal emotion recognition method are completed.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an electroencephalogram signal emotion recognition method based on VMD and WPD, and the method comprises the steps: carrying out the variational mode decomposition of obtainedelectroencephalogram signal data, and obtaining a variational mode component; wavelet packet decomposition and reconstruction are performed on the variational mode components, so that reconstructed signals under beta and gamma frequency bands in the electroencephalogram signal frequency band can be obtained; wavelet packet entropies, improved multi-scale sample entropies, fractal dimensions and first-order difference values of the reconstructed signals are calculated respectively, and feature vectors for electroencephalogram signal emotion recognition are formed; and sending the feature vectorinto a classifier to carry out classification and identification of the emotion state. Compared with the prior art, the method has the advantages that better time-frequency resolution is obtained, nonlinear features of EEG signals can be better captured, EEG frequency bands more related to emotion can be obtained, a good EEG feature basis is provided for emotion recognition, and then a better emotion recognition effect is obtained.

Description

technical field [0001] The present disclosure relates to the technical field of emotion recognition, in particular to a VMD and WPD-based EEG signal emotion recognition method and system. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In recent years, the research on emotion recognition based on EEG (Electroencephalogram, EEG) signal has been quite large-scale, and people's enthusiasm for the development of EEG-based emotion recognition has been increasing. The use of advanced feature extraction methods can realize the emotional interaction between man and machine. , and gradually achieve informatization. Therefore, EEG-based emotion recognition came into being, that is, using EEG for emotion recognition to realize online fatigue monitoring, distance education and assisting doctors in medical diagnosis, etc. The China Brain Project is ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62A61B5/0476A61B5/16A61B5/00
CPCA61B5/165A61B5/168A61B5/7267A61B5/369G06F2218/22G06F2218/08G06F2218/12G06F18/25G06F18/24323G06F18/259
Inventor 郑向伟张敏胡斌张宇昂尹永强
Owner SHANDONG NORMAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com