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A kind of emotion recognition method and system

A technology of emotion recognition and to-be-recognized, which is applied in the field of emotion recognition and can solve the problem of low accuracy of emotion recognition

Inactive Publication Date: 2021-01-05
TAIYUAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, due to the nonlinearity and non-stationarity of emotional EEG signals and the complexity of the interaction of various brain regions in the process of emotional cognition, a single feature or a simple combination of several features cannot effectively represent the differences in EEG induced by different emotions. , the accuracy of emotion recognition is not high

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  • A kind of emotion recognition method and system
  • A kind of emotion recognition method and system
  • A kind of emotion recognition method and system

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Embodiment

[0106] figure 1 It is a flow chart of an emotion recognition method provided by an embodiment of the present invention. Such as figure 1 As shown, an emotion recognition method, including:

[0107] Step 101: Randomly play speech clips of various emotion types to the user, collect the user's EEG signals in each emotion type speech clip, and obtain the EEG signals to be trained. Emotion types include Sad, Angry, Happy, Surprised.

[0108] Step 102: Extract the EEG signal features to be trained.

[0109] Before extracting the features of the EEG signal to be trained, the acquired EEG signal to be trained is preprocessed, and the processing method is the same as the preprocessing method in step 106 .

[0110] The EEG signal features to be trained include: time-frequency domain features to be trained, nonlinear features to be trained, and brain network attribute features to be trained; the time-frequency domain features to be trained are based on wavelet decomposition of the EE...

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Abstract

The invention discloses an emotion recognition method and system. The method comprises the steps of acquiring an electroencephalogram signal to be recognized; extracting to-be-identified time-frequency domain characteristics, to-be-identified nonlinear characteristics and to-be-identified brain network attribute characteristics; wherein the to-be-identified time-frequency domain feature is a wavelet entropy calculated according to a wavelet decomposition coefficient of the to-be-identified electroencephalogram signal; wherein the to-be-identified nonlinear feature comprises a power spectrum density obtained by performing discrete Fourier transform on the to-be-identified electroencephalogram signal and a Hurst index obtained by performing average error calculation on the to-be-identified electroencephalogram signal; wherein the attribute characteristics of the to-be-identified brain network reflect the correlation among the to-be-identified electroencephalogram signals; and carrying out emotion recognition on the to-be-recognized electroencephalogram signal features by adopting a trained support vector classifier. The method and the system provided by the invention have the advantage that the emotion recognition accuracy can be improved.

Description

technical field [0001] The invention relates to the technical field of emotion recognition, in particular to an emotion recognition method and system. Background technique [0002] Emotion is a psychological and physiological reaction process induced by external stimuli. Correct and effective emotion recognition is an important guarantee for people's daily life and communication, and it is also a key problem to be solved in the realization of advanced artificial intelligence. Traditional emotion recognition is mainly based on external behaviors such as facial expressions, voice intonation, and gestures. However, the emotional characteristics reflected in these external expressions are highly subjective and easy to be concealed or disguised. With the development of sensing technology, emotion recognition based on real-time acquisition of electrophysiological signals, such as EEG, ECG, and EMG, is more reliable and effective, especially EEG signals can objectively reflect the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 陈桂军张雪英李凤莲孙颖黄丽霞王杰
Owner TAIYUAN UNIV OF TECH
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