Emotion recognition system and application based on deep learning and brain-computer interface

Abstract

An emotion recognition system and application based on deep learning and brain-computer interface, comprising a sequentially connected portable EEG acquisition device, an emotional EEG classification module and an emotion classification display module, the portable EEG acquisition device collects data from the brain of a subject Emotional EEG signal, the emotional EEG classification module analyzes the emotional EEG signal, establishes a relationship model between the emotional EEG signal and emotion, and through the relationship model, the input emotional EEG signal is analyzed. Emotional classification, the emotional classification display module displays the recognized emotional types and prompts the emotional state of the subject. The emotion recognition system and application based on deep learning and brain-computer interface of the present invention can realize accurate acquisition, effective identification and correct classification of emotional EEG signals, intuitively prompt emotional states, and realize the function of emotional state monitoring. The invention directly establishes the corresponding relationship between the EEG signal and the emotion type, and realizes the feedback to the emotion.

Description

technical field [0001] The present invention relates to an emotion recognition system. In particular, it relates to an emotion recognition system and application based on deep learning and brain-computer interface. Background technique [0002] Emotion is the reaction of people when they are stimulated by external things, and it is a broad and complex physiological and psychological state. There are many kinds of human emotional states, which are roughly defined by psychologist Ekman as happiness, anger, surprise, fear, disgust and sadness. In fact, there are many other emotional states such as shame, arrogance, disappointment, anxiety, etc. A person's emotional state often reflects a person's attitude and views on external things. Emotion recognition can help people improve the safety of device use and analyze potential emotional factors in daily behavior. For example, in rehabilitation medicine, it can help doctors diagnose and prevent problems such as depression and po...

AI Technical Summary

Problems solved by technology

However, in practical applications, there are still many challenges in using EEG signals for emotional analysis.

First of all, the EEG acquisition process is relatively difficult. To obtain stable EEG signals, there are certain requirements for the acquisition equipment, subjects, and external environment.

Although implantable and semi-implantable EEG electrodes can obtain more stable signals, their operation is complicated and their daily use is less feasible

At present, non-implantable EEG electrodes are generally used in the industry. Although they have the advantages of portability and simple operation, they are easily affected by external noise.

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