Emotion recognition method and system based on deep learning model and long-short memory network

Abstract

The invention discloses an emotion recognition method and system based on a deep learning model and a long-short memory network, The method comprises the following steps: data preprocessing and data set partitioning of EEG signals are performed to construct a network model, wherein the network model comprises a picture reconstruction model composed of a variational encoder and an emotion recognition model composed of a long-short memory network; the network model comprises an image reconstruction model composed of a variational encoder and a short-long memory network; The objective function isconstructed according to the network model. The network model is trained by training set, and the objective function is optimized by Adam optimizer in neural network, and the trained network model isobtained. Using the cross-test set to cross-test the trained network model, determining the super-parameters of the network model, and obtaining the final network model; and using the final network model to visualize the seed data and perform emotion recognition. The invention relies on the data artificial intelligence method to learn the collected EEG signal space and time complex structure, reduce the subjective factors in the prediction, and improve the prediction accuracy.

Description

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AI Technical Summary

Benefits of technology

This patented describes a way that uses advanced neural models (ANN) or generative adversarial nets (GNN), which were developed from previous work done before 1990 when Neural Network Theory was proposed. These ANNs learn patterns within large amounts of raw brain electrical activity without being explicitly stated about them. They then predict how different emotions may be experienced over longer periods of time - they create images called electroencephalograms ("EEG"). By analyzing these images, we found that there existed certain relationships between their content and those associated with negative feelings such as sadness or excitement. Overall, our approach allows us to accurately identify various aspects related to mental states like feeling good/fellowing up, moodiness, etc., while also reducing subjectivity issues involved during analysis.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving understanding how humans generate feeling when interacting with their surroundings during physical activities such as driving cars on streets without being influenced from other individuals that may also cause them to feel unhealthy for longer periods due to environmental influences like sunlight exposure.

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