Electroencephalogram emotion recognition method based on multi-task capsule

Abstract

The invention discloses an electroencephalogram emotion recognition method based on multi-task learning. The electroencephalogram emotion recognition method comprises the steps of 1, preprocessing of baseline removal and fragment segmentation on original EEG data; 2, establishing a multi-task learning model; 3, performing channel attention processing on the original EEG signal; 4, constructing a multi-task capsule network model; 5, training the established multi-task capsule network model on a public data set by adopting a ten-fold crossing method; and 6, realizing an emotion classification task by utilizing the established model. According to the invention, 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 a multi-task-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 kinds of research applied to emotion recognition. People’s emotions are commonly judged by facial expressions, language, and body movements. Among them, there are real-time differences in the electroencephalogram (EEG) chamber, but it is different from human emotional states. There is close connection, so the present invention adopts the emotion recognition research method based on EEG signal. EEG emotion recognition algorithms are mainly divided into two categories: traditional algorithms and algorithms based on deep learning. [0003] In traditional algorithms for emotion recognition based on EEG signals, features are usually extr...

AI Technical Summary

Problems solved by technology

[0005] At present, most of the end-to-end emotion recognition methods based on deep learning are single-task learning to extract the features of EEG signals, and the existing deep learning methods are all based on single-task learning, but this method cannot consider EEG. The relevant information between all tasks of the signal, of course, does not take into account the sharing of complementary information between the tasks of the EEG signal, which reduces the accuracy of EEG recognition, so the expected recognition rate cannot be achieved

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