Electroencephalogram emotion recognition method based on attention mechanism

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 ...

AI Technical Summary

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 of the signal, resulting in low recognition accuracy

In addition, the relevant features extracted by the current EEG emotion recognition method based on deep learning not only lack the global spatial information, but also do not consider the time dependence of the sample, so it cannot achieve a higher recognition rate.

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