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Multi-modal feature fusion electroencephalogram emotion recognition method based on multi-scale imaging

A feature fusion and emotion recognition technology, applied in character and pattern recognition, neural learning methods, sensors, etc., can solve problems such as poor model generalization ability, limited classification performance, and loss of EEG spatial information, so as to reduce the amount of calculation Effect

Pending Publication Date: 2022-07-22
ZHENGZHOU UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a multi-modal feature fusion EEG emotion recognition method based on multi-scale imaging. The problem of severe loss of graph space information and limited classification performance

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  • Multi-modal feature fusion electroencephalogram emotion recognition method based on multi-scale imaging
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  • Multi-modal feature fusion electroencephalogram emotion recognition method based on multi-scale imaging

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Embodiment Construction

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0031] like figure 1 As shown, the multi-modal feature fusion EEG emotion recognition method based on multi-scale imaging of the present invention includes the following steps: including the following steps:

[0032] S1, use the python code to remove the baseline of the original EEG signal to obtain the first EEG signal;

[0033] In this embodiment, the EEG signal data set is downloaded from the public DEAP as raw data. In the DEAP database, 32 participants participated in the experiment. Each participant was asked t...

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Abstract

The invention discloses a multi-modal feature fusion electroencephalogram emotion recognition method based on multi-scale imaging, multi-scale and time sequence imaging algorithms are combined, electroencephalogram signals are converted into images to achieve emotion recognition, and compared with a traditional emotion recognition method based on EEG signals, space information of the electroencephalogram signals can be stored, and the recognition accuracy of the electroencephalogram signals is improved. In addition, a multi-scale algorithm can be used for reducing the calculation amount and finding a potential electroencephalogram signal mode, meanwhile, high-dimensional information is coded into the image, the image is made to contain rich information, the advantages of machine vision are fully utilized, a 2DCNN model is used for extracting high-dimensional features of the image, and a better sentiment classification result is obtained through different multi-mode feature fusion methods.

Description

technical field [0001] The invention relates to the field of physiological signal processing, in particular to a multi-modal feature fusion EEG emotion recognition method based on multi-scale imaging. Background technique [0002] Emotion is a complex psychological and physiological state, which affects people's cognition, behavior and interpersonal communication. According to cognitive and neurophysiological theories, emotions, which play an important role in human brain activity, can be detected in the brain's electroencephalogram (EEG) signals. Therefore, effective emotion recognition can be performed using EEG signals. [0003] The traditional emotion recognition method based on EEG signal mainly uses 1DCNN (Chinese interpretation: one-dimensional convolution) technology to extract the signal features of EEG, and train a classifier to realize emotion recognition. This traditional emotion recognition method only focuses on the time domain or frequency domain information...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/372A61B5/00A61B5/16G06K9/00G06N3/04G06N3/08
CPCA61B5/372A61B5/7267A61B5/165G06N3/08G06N3/045G06F2218/12
Inventor 徐华兴胡飞常加兴毛晓波李立国郑鹏远
Owner ZHENGZHOU UNIV
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