Electroencephalogram emotion migration model training method and system and electroencephalogram emotion recognition method and device

Abstract

The invention belongs to the technical field of electroencephalogram recognition, particularly relates to an electroencephalogram emotion migration model training method and system and an electroencephalogram emotion recognition method and device. The problem of electroencephalogram emotion migration is solved, and the difficulty and cost of electroencephalogram emotion recognition training are reduced. An electroencephalogram emotion recognition migration model based on a depth domain adversarial network is built; electroencephalogram features are mapped into an electroencephalogram feature topological graph; deep features are extracted from a feature image by using a deep convolutional neural network; the extracted deep features are input into domain adaptation networks; the two-stage domain adaptation networks are utilized; the first stage performs preliminary confusion on a source domain and a target domain by adopting a maximum mean value difference; and the second-stage domain adaptation network increases the between-class distance, so that the migration model performance and the emotion recognition accuracy are improved. According to the invention, the problem of electroencephalogram emotion migration can be effectively solved, and the application effect of the migration model in emotion recognition is improved. The relatively good application prospects are realized.

Description

technical field [0001] The invention belongs to the technical field of EEG recognition, in particular to a method and system for training an emotional EEG migration model based on a two-layer domain adaptation network, and an EEG emotion recognition method and equipment. Background technique [0002] Emotion recognition plays an important role in human-computer interaction systems. In addition, accurately identifying patients' emotions can help improve the quality of care. The current popular emotion detection can be divided into two categories. One class is based on non-physiological signals. such as facial expressions. The other category is based on psychological signals. such as EEG signals. Facial expressions are prone to false expressions, while EEG signals are directly extracted from the cerebral cortex without loss, which can directly reflect the physiological state of the human brain. Therefore, emotion recognition technology based on EEG signals has received e...

AI Technical Summary

Problems solved by technology

[0004] To this end, the present invention provides an emotional EEG migration model training method and system based on a two-layer domain adaptation network, and an EEG emotion recognition met...

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