Electroencephalogram emotion recognition method based on attention mechanism

An emotion recognition and attention technology, applied in the field of emotion computing, can solve the problems of inability to achieve recognition rate, lack of global spatial information, ignoring the time dependence of EEG signals, etc., and achieve the effect of improving the accuracy of emotion recognition.

Active Publication Date: 2019-12-24
HEFEI UNIV OF TECH
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Problems solved by technology

[0005] At present, most of the end-to-end emotion recognition methods based on deep learning use convolutional neural networks to extract the local spatial features of EEG signals. This method fails to consider the global spatial information of all channels of EEG signals, and also ignores the EEG Time dependence o

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  • Electroencephalogram emotion recognition method based on attention mechanism
  • Electroencephalogram emotion recognition method based on attention mechanism
  • Electroencephalogram emotion recognition method based on attention mechanism

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Embodiment Construction

[0037] In this embodiment, a method of EEG signal emotion recognition based on attention mechanism mainly uses convolutional neural network (CNN) and channel attention mechanism (Channel-wise attention) to extract the spatial information in the original EEG signal, and then uses The recurrent neural network (RNN) and the self-attention mechanism (Self-attention) extract the time information in the encoded sample, and finally obtain the spatio-temporal attention features of the EEG signal to achieve classification, such as figure 1 As shown, proceed as follows:

[0038] Step 1. Obtain the EEG signal data with R emotional labels of any subject A and perform preprocessing, including de-baseline and sample segmentation, so as to obtain N EEG signal samples of subject A, denoted as S={S 1 ,S 2 ,...,S k ,...,S N}, where S k ∈R m×P Indicates the kth EEG signal sample, m indicates the channel number of the EEG signal, P indicates the number of sampling points, k=1,2,...,N; in th...

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Abstract

The invention discloses an electroencephalogram signal emotion recognition method based on an attention mechanism. The electroencephalogram signal emotion recognition method comprises the steps of 1,carrying out the preprocessing of removing the baseline and segmenting the fragments on the original EEG data; 2, establishing a space-time attention neural network model; 3, training the establishedconvolutional recurrent attention network model on a public data set by adopting a ten-fold crossing method; and 4, realizing an emotion classification task by utilizing the established model. According to the invention, the high-precision emotion recognition can be realized, so that the recognition rate is improved.

Description

technical field [0001] The invention relates to the field of emotion computing, in particular to an attention mechanism-based EEG signal emotion recognition method. Background technique [0002] Emotion is an indispensable part of people's daily life, and emotion recognition is also a key technology in the field of artificial intelligence. There are many research methods applied to emotion recognition. People’s expressions, language, and body movements are commonly used to judge people’s emotions. Electroencephalogram (EEG) signals have real-time differences and are closely related to people’s emotional states. The research method of emotion recognition based on EEG signal is adopted. EEG emotion recognition algorithms are mainly divided into two categories: traditional algorithms and algorithms based on deep learning. [0003] In the traditional algorithm of emotion recognition based on EEG signal, the extraction feature is usually designed from the EEG signal first, and ...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08A61B5/16A61B5/0476A61B5/00
CPCG06N3/08A61B5/168A61B5/7264A61B5/369G06N3/045G06F2218/08G06F2218/12
Inventor 陈勋陶威李畅成娟宋仁成刘羽
Owner HEFEI UNIV OF TECH
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