Emotion classifying method based on facial expression and EEG

Abstract

The invention relates to an emotion classifying method based on facial expression and EEG, and belongs to the technical field of pattern identification. The emotion classifying method comprises the steps of firstly synchronously collecting facial expression and electroencephalogram (EEG) data, then cutting off EEG data implying emotion information through the change information of the facial expression, and finally performing emotion classification through EEG information after treatment with an emotion classifying algorithm. Compared with an emotion calculating method which is frequently usedat present, the emotion classifying method based on facial expression and EEG provided by the invention has the advantages that facial expression identification is additionally performed in the emotion classifying process to pretreat the EEG data, and emotion components in the EEG data are improved, so that the emotion classifying accuracy rate is increased.

Description

technical field [0001] The invention relates to a multimodal information fusion method, in particular to an emotion classification method based on facial expression and electroencephalogram (EEG), and belongs to the technical field of intelligent pattern recognition. Background technique [0002] With the development of information technology and artificial intelligence, scientists have proposed the concept of "emotional computing". The informatics community has conducted research on emotion acquisition, emotion analysis and recognition, emotion understanding and expression (collectively referred to as affective computing or emotion recognition). As an important part of brain-like intelligence, emotion recognition has important application value in social emotion mining analysis, wearable computing, computer-aided learning, etc. [0003] People have gradually realized that the use of human physiological indicators (cortisol level, heart rate, blood pressure, respiration, sk...

AI Technical Summary

Problems solved by technology

Although domestic and foreign researchers have done a lot of sufficient and detailed work from emotion induction, emotion feature extraction, classification to multimodal emotion recognition, the accuracy of emotion classification is still difficult to achieve practicality.

The reason is that emotion computing involves the intersection of multiple disciplines, the non-stationarity of EEG signals makes feature extraction very difficult, and the specificity of emotion generation brings difficulties to emotion classification methods.

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