Fine-grained visualization system and method for emotional electroencephalography (EEG)

Abstract

The invention discloses a fine-grained visualization system and method for emotional electroencephalography (EEG), and solves the technical problem of how to display fine-grained information in the emotional EEG. The system is connected with a data acquisition module, a data preprocessing module, a feature extraction module and a network training control module in sequence; an expression atlas provides a target image; the network training control module and an affective computing generative adversarial network (AC-GAN) module complete the training of an AC-GAN; a network forward execution module controls to complete the generation of fine-grained expressions. The method comprises the steps of collecting emotional EEG data, preprocessing the EEG data, extracting EEG features, constructing the AC-GAN, preparing the expression atlas, training the AC-GAN, and obtaining a fine-grained facial expression generation result. The emotional EEG is directly visualized into facial expressions withthe fine-grained information which can be directly recognized, and the visualization system is used for interactive enhancement and experience optimization of rehabilitation equipment, emotional robots, VR devices and the like with a brain-computer interface.

Description

technical field [0001] The invention belongs to the field of information technology, and further relates to the fine-grained visualization of emotional brain electricity (EEG) by applying a generative confrontation network (GAN) in a biological intersection technology. Specifically, it is a fine-grained visualization system and method of emotional EEG, which generates facial expression images with fine-grained emotional intensity information from EEG data. Facial expression is a form of information that humans can directly recognize, which can enhance the interactive performance and experience of related devices. Background technique [0002] Affective computing is a biological crossover technology that has been widely studied in recent years. Its purpose is to enable machines to accurately recognize and present human emotional states, also known as emotional intelligence. The research on EEG-based affective computing mainly focuses on how to effectively induce people's emo...

AI Technical Summary

Problems solved by technology

Traditional EEG-based emotion recognition fails to recognize fine-grained emotional states because of the lack of EEG data with fine-grained emotional labels, and it is extremely difficult to label emotional EEG in a fine-grained manner.

If the subject’s subjective labeling method of emotional EEG fine-grained labels is used, the labeling task will affect the subject’s current emotional experience and affect the experiment; if the method of manually labeling fine-grained labels after the experimen

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