Brain emotion identification method based on multi-classifier fusion model constructed via hierarchical mechanism

A multi-classifier fusion and emotion recognition technology, which is applied in the field of emotion computing, can solve the problems that the accuracy of emotion recognition methods needs to be improved, and achieve the effects of improving the accuracy of emotion recognition, high recognition ability, and reducing computing costs and memory consumption

Active Publication Date: 2017-06-23
BEIJING UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0006] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to propose an EEG emotion recognition method based on a layered mechanism to construct a multi-classifier fusion

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  • Brain emotion identification method based on multi-classifier fusion model constructed via hierarchical mechanism
  • Brain emotion identification method based on multi-classifier fusion model constructed via hierarchical mechanism
  • Brain emotion identification method based on multi-classifier fusion model constructed via hierarchical mechanism

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Embodiment

[0092] The EEG emotion recognition method based on layered mechanism of the present invention to build multi-classifier fusion and the traditional recognition method based on a single classifier are compared and verified below, and the experimental parameters are selected as follows:

[0093] The simulation data is selected from the EEG emotional data in the public dataset DEAP. A total of 32 subjects participated in the data collection, aged between 19 and 37, and each subject was required to watch 40 short music videos. During the emotion-induced experiment, a two-dimensional emotion model was used to quantify emotion, including two dimensions of arousal (Arousal) and valence (Valence). After watching a video, each subject needs to record the measurement value of each dimension in the self-assessment scale (SAM), and the value range is 1-9. The EEG signals were collected with a 32-conductor electrode cap of the International 10-20 system, and the sampling frequency was 512 H...

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Abstract

The invention relates to a brain emotion identification method based on a multi-classifier fusion model constructed via a hierarchical mechanism. Multi-channel emotion brain data is collected and analyzed by brain preprocessing, feature extraction and weight measuring channel selection to construct an emotion brain feature matrix. Channel division is carried out on the emotion brain feature matrix according to electrode positions, optimized feature selection integration is carried out on each channel, and single-emotion classification models are formed. The difference and accuracy of the classification models in solving the same emotion identification problem serve as evaluation criteria, an optimal single-emotion classification model is selected from each channel, and a classifier set to be fused is obtained. An emotion identification fusion model is constructed on the basis of a weighted voting method by utilizing classification errors of the optimal single-emotion classification models serve as weights. According to the invention, multi-classifier fusion is used to solve problem that a high emotion identification rate is hard to obtain in a brain sample space.

Description

technical field [0001] The present invention relates to the field of emotion computing, and relates to an emotion recognition method based on EEG, in particular to an EEG emotion recognition method based on channel layering mechanism and feature selection integration to build multi-classifier fusion. Background technique [0002] Emotion is an advanced function of the human brain. It is a psychological and physiological state that accompanies the process of cognition and consciousness. It integrates people's feelings, thoughts and behaviors, and plays a very important role in the communication between people. In recent years, with the rapid development of ubiquitous technology and computer technology, emotion recognition, as a key issue of affective computing, has become an important interdisciplinary research topic in the fields of computer science, cognitive science and artificial intelligence, and has received more and more attention. more and more attention and applicati...

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Application Information

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IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/2411
Inventor 李贤闫健卓李东佩盛文瑾王静陈建辉
Owner BEIJING UNIV OF TECH
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