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

Emotion recognition method and system based on three-dimensional feature map and convolutional neural network

A convolutional neural network and emotion recognition technology, which is applied in the field of emotion recognition methods and systems based on three-dimensional feature maps and convolutional neural networks, can solve the problems of small contribution, small amount of data, and provide spatial information to ensure accuracy. , improve the accuracy, the recognition effect is good

Active Publication Date: 2021-06-18
SHANDONG HAILIANG INFORMATION TECH RES INST +1
View PDF4 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The EEG is composed of one-dimensional signals generated by each channel and cannot provide spatial information for emotion recognition
At present, CNN is applied in the field of EEG-based emotion recognition, but its recognition accuracy is relatively low. One of the reasons is that it fails to provide spatial relative position information between EEG channels; second, the amount of data is relatively small, and the model The probability of overfitting increases; third, the extracted EEG features make little contribution to emotion recognition

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
  • Emotion recognition method and system based on three-dimensional feature map and convolutional neural network
  • Emotion recognition method and system based on three-dimensional feature map and convolutional neural network
  • Emotion recognition method and system based on three-dimensional feature map and convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] In this embodiment, an emotion recognition method based on a three-dimensional feature map and a convolutional neural network is disclosed, including:

[0040] Obtain the EEG signal to be identified;

[0041] Extract the EEG signal without the underlying emotional state from the EEG signal to be identified;

[0042] Using wavelet packet transform to decompose and reconstruct the EEG signal without basic emotional state, obtain multiple frequency band signals, and obtain the wavelet energy ratio and wavelet entropy of each frequency band signal;

[0043] Obtain the complexity of each channel EEG signal in the EEG signal without a basic emotional state, and form the EEG feature with the wavelet energy ratio and wavelet entropy of each frequency band signal;

[0044] Arrange the EEG features to form a feature cube;

[0045] Input the feature cube into the trained CNN model for emotion recognition.

[0046] Further, the method of eliminating the basic emotional state is ...

Embodiment 2

[0122] In this embodiment, an emotion recognition system based on a three-dimensional feature map and a convolutional neural network is disclosed, including:

[0123] The data acquisition module is used to obtain the electroencephalogram signal to be identified;

[0124] The basic emotional state elimination module is used to extract the non-basic emotional state EEG signal from the EEG signal to be identified;

[0125] The EEG feature acquisition module is used to decompose and reconstruct the EEG signal without a basic emotional state by using wavelet packet transform, obtain multiple frequency band signals, obtain the wavelet energy ratio and wavelet entropy of each frequency band signal, and obtain the EEG signal without a basic emotional state. The complexity of the EEG signal of each channel in the electrical signal, and the wavelet energy ratio and wavelet entropy of each frequency band signal form the EEG feature;

[0126] The feature cube acquisition module is used t...

Embodiment 3

[0129] In this embodiment, an electronic device is disclosed, including a memory, a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the method based on Steps described in the method for emotion recognition with 3D feature maps and convolutional neural networks.

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 emotion recognition method and system based on a three-dimensional feature map and a convolutional neural network. The method comprises the following steps: acquiring a to-be-recognized electroencephalogram signal; extracting a basic emotional state-free electroencephalogram signal from the to-be-recognized electroencephalogram signal; adopting wavelet packet transformation to decompose and reconstruct the electroencephalogram signals without the basic emotional state, obtaining multiple frequency band signals, and obtaining the wavelet energy ratio and the wavelet entropy of each frequency band signal; obtaining the complexity of each channel electroencephalogram signal in the electroencephalogram signals without the basic emotional state, and forming electroencephalogram features by the complexity of each channel electroencephalogram signal, the wavelet energy ratio of each frequency band signal and the wavelet entropy of each frequency band signal; arranging the electroencephalogram features to form a feature cube; and inputting the feature cube into a trained CNN model for emotion recognition. The accuracy of emotion recognition is improved.

Description

technical field [0001] The invention relates to the technical field of emotional state recognition, in particular to an emotion recognition method and system based on a three-dimensional feature map and a convolutional neural network. 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] Emotion is a state that integrates human feelings, thoughts, and behaviors, and is very important in human decision-making processing, interaction, and cognitive processes. Currently, most studies on emotional state classification use electroencephalogram (Electroencephalogram, EEG) and facial expressions to classify emotional states. EEG is a signal that records activity on the surface of the cerebral cortex, the result of synaptic activation of neurons in the brain. It has been shown in recent years that EEG is a suitable signal for biometric authentication...

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): G06K9/00G06F3/01G06N3/04G06N3/08
CPCG06F3/015G06N3/08G06N3/045G06F2218/08G06F2218/12
Inventor 郑向伟尹永强崔振陈宣池张利峰许春燕张宇昂高鹏志
Owner SHANDONG HAILIANG INFORMATION TECH RES INST
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