Emotion recognition system based on depth learning and brain-computer interface as well as application

Abstract

The invention discloses an emotion recognition system based on depth learning and a brain-computer interface as well as application. The emotion recognition system comprises portable EEG acquisition equipment, an emotion EEG classification module and an emotion classification display module, wherein the portable EEG acquisition equipment acquires emotion EEG signals from the brain of a subject; the emotion EEG classification module analyzes the emotion EEG signals, establishes a relation model between the EEG signals and emotions, and performs emotion classification on the input emotion EEG signals through the relation model; and the emotion classification module displays recognized emotion types and prompts the emotion state of the subject. The emotion recognition system based on depth learning and the brain-computer interface as well as application can realize accurate acquisition, effective identification and accurate classification of the emotion EEG signals, directly promote the emotion state and realize an emotion state monitoring function. The corresponding relation between the EEG signals and the emotion types is directly established, and feedback of emotions is realized.

Description

technical field [0001] The 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 when people are stimulated by external things, and it is a broad and complex physiological and psychological state. There are many kinds of human emotional states, and psychologist Ekman roughly defines them as six kinds of 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 equipment 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 post-...

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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