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Electroencephalogram signal emotion recognition method based on dimension model

A technology for EEG signal and emotion recognition, applied in medical science, psychological devices, sensors, etc., can solve problems such as poor generalization ability and low accuracy rate

Active Publication Date: 2021-04-16
SHANXI UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0010] In order to solve the problems of low accuracy and poor generalization ability of EEG signal emotion recognition based on dimensional model, the present invention provides a kind of EEG signal based on dimensional model Signal Emotion Recognition Methods

Method used

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  • Electroencephalogram signal emotion recognition method based on dimension model
  • Electroencephalogram signal emotion recognition method based on dimension model
  • Electroencephalogram signal emotion recognition method based on dimension model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0091] EEG signal and emotion label preprocessing:

[0092] Select the 8-lead EEG data of the symmetrical left and right brain hemispheres associated with emotions for research. The lead selection is as follows: figure 2 shown. The corresponding leads are Fp1, Fp2, F3, F4, P3, P4, O1, O2. The emotion-induced EEG signal was obtained by subtracting the first 3s data from the last 60s data of the 63s EEG data.

[0093] The two emotional dimensions of valence and arousal are used to classify positive and negative emotions. The present invention selects two emotional label thresholds for valence and arousal to divide emotional states in dimensions. The first type of situation: the emotional label score greater than 7 is regarded as high valence and high arousal, and less than 3 is regarded as low valence and low arousal; the second type of situation: the emotional label score greater than 6 is regarded as high valence and high arousal, and less than 4 is regarded as low Potency...

Embodiment 2

[0097] EEG feature extraction: including frequency domain features, time-frequency features and nonlinear features.

[0098] 1. Frequency domain characteristics

[0099] (1) Map the EEG signal to the four rhythmic frequency bands of θ, α, β, and γ;

[0100] (2) Obtain the power spectrum of the EEG signal by using the AR model method;

[0101] (3) Use the Burg algorithm to quickly realize the power spectrum parameter estimation;

[0102] (4) Calculate the spectral energy corresponding to each rhythm frequency band of the EEG signal as the frequency domain features of the EEG θ, α, β and γ rhythms.

[0103] 2. Time-frequency characteristics

[0104] The EEG signal s(t) is decomposed by the wavelet packet, and the jth layer gets 2 j equal-bandwidth subspaces, subspaces The subsignals of are:

[0105]

[0106] in, for the subspace The wavelet packet decomposition coefficient of , ψ j,k (t) is a wavelet function. The signal s(t) can be refactored as:

[0107]

...

Embodiment 3

[0134] Stacked self-encoded emotion classification and recognition

[0135] 1. Training stacked self-encoder neural network:

[0136] First, the sample data is randomly divided into 5 parts, 4 parts are randomly selected as the training set, and the remaining 1 part is used as the test set.

[0137] The sparse autoencoder is a multi-layer deep neural network composed of an input layer, a hidden layer, and an output layer. The layer-by-layer greedy training method is used to make the output value of the autoencoder infinitely close to the input value. The input layer nodes are EEG feature vectors, the number of output layer nodes is 2, the number of neuron nodes in the hidden layer of the first sparse autoencoder is 15, and the number of neuron nodes in the hidden layer of the second sparse autoencoder is 7.

[0138] The steps to train a stacked autoencoder neural network are:

[0139] Step 1: Input training data n is the number of samples, and m is the feature dimension of...

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Abstract

The invention belongs to the technical field of emotion calculation and emotion recognition, and particularly relates to an electroencephalogram signal emotion recognition method based on a dimension model. Aiming at the technical problem of low positive and negative emotion classification accuracy under a dimension model at present, the electroencephalogram signal emotion recognition method based on the dimension model comprises the following steps: (1) electroencephalogram preprocessing; (2) respectively extracting frequency domain, time frequency and nonlinear characteristics of the preprocessed multi-lead electroencephalogram signals; and (3) conducting emotion classification of a stack type self-encoding neural network. The classification method provided by the invention is stable and reliable, explains the influence of data balance, feature combination and emotion label threshold on electroencephalogram signal emotion recognition, and improves the accuracy of electroencephalogram signal positive and negative emotion classification under the dimension model.

Description

technical field [0001] The invention belongs to the field of emotion computing and emotion recognition, and in particular relates to a method for recognizing emotion of EEG signals based on a dimensional model. Background technique [0002] According to the report of the World Health Organization, 70% of chronic diseases are caused by stress, anxiety and stress factors. Excessive anxiety has become a chronic disease, which brings huge burdens and hidden dangers to families and society. It is of great significance to improve the quality of life for sub-health diseases. At present, there are more than 90 million depressed people in China, accounting for 7.3% of the total population. In 2020, depression will become the second largest disease after cardiovascular disease, and it is expected to rise to become the disease with the largest burden in China in 2030. [0003] Human-computer emotional interaction endows machines with the ability to observe, understand, and generate va...

Claims

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Application Information

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IPC IPC(8): A61B5/369A61B5/378A61B5/38A61B5/16A61B5/00
CPCY02D30/70
Inventor 乔晓艳刘鹏
Owner SHANXI UNIV
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