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

Emotion recognition method based on deep fuzzy forest

An emotion recognition and forest technology, applied in the field of emotion recognition, can solve the problems of complex background noise, large amount of data, and high data dimension, and achieve the effect of high recognition degree, few hyperparameters, and enhanced representation learning ability.

Inactive Publication Date: 2020-04-14
SHANGHAI NORMAL UNIVERSITY
View PDF3 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the EEG signal is a random non-stationary weak signal, usually only 0.2 to 1 millivolt, and has the characteristics of strong randomness and non-stationarity, nonlinearity, and complex background noise. Electrical data has high dimensions and a large amount of data

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 based on deep fuzzy forest
  • Emotion recognition method based on deep fuzzy forest
  • Emotion recognition method based on deep fuzzy forest

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] Such as figure 1 As shown, this embodiment is an emotion recognition method based on a deep fuzzy forest, which mainly includes the steps of music emotion EEG data collection, EEG signal preprocessing, and deep fuzzy forest music emotion recognition. Among them, the music EEG data collection is through the establishment of the EEG music emotion experiment. Firstly, a quiet and insufficiently lit experimental environment is selected, and then the subjects are divided into two parts. One half is used to select experimental materials, and the other half is tested to obtain EEG data based on musical emotion. The EEG signal preprocessing process includes three parts: filtering, segmentation and screening samples. Deep fuzzy forest emotion recognition is the process of classifying music EEG data, including two processes of multi-granularity scanning to extract features and cascading forest emotion recognition.

[0053] Such as Figure 8 As shown, the above steps are descri...

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 relates to an emotion recognition method based on a deep fuzzy forest. The method comprises the following steps: S1, collecting electroencephalogram signals; S2, preprocessing the electroencephalogram signals to remove noise; S3, inputting the electroencephalogram signals into a pre-trained deep fuzzy forest model to obtain an emotion recognition result. In the step S3, multi-granularity scanning is adopted for the deep fuzzy forest model, and probability vectors of electroencephalogram signal features are obtained from the electroencephalogram signals to serve as input of a cascade forest; and a cascade forest is adopted to recognize the probability vectors of the electroencephalogram signal features to obtain an emotion recognition result, and the multi-granularity scanningand the cascade forest are both constructed by adopting a fuzzy decision tree. Compared with the prior art, the emotion recognition method combines a fuzzy set theory with a traditional decision treelearning strategy, and has the advantages of originality, high recognition degree, few parameters, capability of being used for a small sample data set, accurate and reliable result and the like.

Description

technical field [0001] The invention relates to the field of emotion recognition, in particular to an emotion recognition method based on a deep fuzzy forest. Background technique [0002] With the development of science and technology, people's lives are becoming more and more enriched, and the integration of interdisciplinary knowledge and technology has made the research methods of psychological related diseases more and more diversified. Emotion is a kind of psychological state produced by integrating people's feelings, thoughts and desires. It is ubiquitous in people's daily life, work and study. Bad emotions will affect our physical health and mood. Cause depression, anxiety, etc., and have a major impact on interpersonal communication. In the research and treatment of these diseases, music has also played an increasingly important role, and now a special discipline of "music therapy" has been formed. The so-called "music therapy" is to study the influence of music o...

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
IPC IPC(8): A61B5/0476A61B5/16A61B5/00G06K9/62
CPCA61B5/165A61B5/7203A61B5/7267A61B5/725A61B5/369G06F18/2148G06F18/24323G06F18/2415
Inventor 何宏姚慧芳谭永红
Owner SHANGHAI NORMAL UNIVERSITY
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