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Emotion recognition method based on brain-computer generative adversarial

An emotion recognition and brain-computer technology, which is applied in a variety of biometrics, biometrics, neural learning methods, etc., can solve the problems of inconvenient data collection and large data volume requirements, so as to improve the recognition effect, reduce the probability and cost, the effect of enhancing the capacity for emotional learning

Active Publication Date: 2022-01-28
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

Using the powerful intelligent form of brain-computer collaboration can not only retain the advantages of the human brain and machines, but also solve the problems of the former’s inconvenient data collection and the latter’s large data volume requirements, and realize brain-computer collaborative work

Method used

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  • Emotion recognition method based on brain-computer generative adversarial
  • Emotion recognition method based on brain-computer generative adversarial
  • Emotion recognition method based on brain-computer generative adversarial

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Embodiment Construction

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] Such as figure 1 , 2 As shown in and 3, an emotion recognition method based on brain-machine confrontation, the specific process is as follows:

[0047] Step 1. Get data

[0048] All the facial emotion images involved in this experiment come from the Chinese Facial Affective Picture System (CFAPS). The emotion images in the system mainly include 7 types of basic expressions, and a total of 870 emotional face images were collected. Among them, 74 were angry, 47 were disgusted, 64 were fearful, 95 were sad, 120 were surprised, 222 were neutral, and 248 were happy.

[0049] In order to introduce human cognitive ability, image-induced EEG features are added on the basis of image recognition by traditional machines to obtain advanced emotional representation. Since the acquisition of EEG emotional features requires the stimulation experiment of emotional images o...

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Abstract

The invention discloses an emotion recognition method based on brain-computer generative adversarial. For an emotion recognition task, cognitive representation with emotions is obtained after the human brain processes information, while a machine only learns representation in an image form. If a generation relation between the cognitive representation with emotions and the representation in an image form can be established, a machine can be guided to learn through the human brain and endowed with the emotion cognitive ability of the human brain. In order to give information perceived by a human brain to a machine, a generation model from image visual features to electroencephalogram emotion features needs to be established, so that generation from formal representation to cognitive representation is realized. According to the method, the relation between electroencephalogram emotion features and image visual features is explored, the relation between formal representation and cognitive representation is established, and brain-computer collaborative intelligence is achieved. In order to reduce the difference between cognitive representation and formal representation and enable the formal representation to infinitely approach the cognitive representation, the invention designs an emotion recognition method based on brain-computer generative adversarial.

Description

technical field [0001] The invention belongs to the intersecting field of brain-computer collaboration and emotion recognition, and specifically relates to a method for emotion recognition based on brain-computer collaboration intelligence technology based on a generative confrontation network. Background technique [0002] Brain-computer collaboration is an important way to achieve a more powerful form of intelligence in the artificial age. Emotion recognition is an important interdisciplinary research topic involving neuroscience, psychology, cognitive science, computer science and artificial intelligence. Generative Adversarial Networks (GAN), a deep learning model, is one of the most promising approaches for unsupervised learning on complex distributions in recent years. Emotion recognition technology based on machine learning. In recent years, breakthroughs have been made in deep learning algorithms applied in the field of computer vision, including convolutional neur...

Claims

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Application Information

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IPC IPC(8): A61B5/16A61B5/369A61B5/378A61B5/00G06K9/62G06N3/04G06N3/08G06V40/70G06V10/764G06V10/766G06V10/774G06V10/82
CPCA61B5/165A61B5/369A61B5/378A61B5/7235A61B5/7267A61B5/7203A61B5/725G06N3/088G06N3/047G06N3/048G06N3/045G06F18/2155G06F18/24Y02D10/00
Inventor 孔万增刘栋军潘泽宇金宣妤郭继伟刘可白云
Owner HANGZHOU DIANZI UNIV
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