Effective time sequence and electrode recombination based electroencephalograph signal categorizing system and method

Abstract

The invention discloses an effective time sequence and electrode recombination based electroencephalograph signal categorizing system and a method, which can realize the identification to three types of character expression (happiness, normality and sadness) by acquiring and analyzing the electroencephalograph signals of the human brain, and mainly comprises an electroencephalograph signal acquisition process and an electroencephalograph signal analyzing process. According to the invention, the electroencephalograph signals are acquired by stimulating different expression of a testee; the feature space of the effective electroencephalograph signals is determined by the strong energy distribution of the full field of the electroencephalograph signals; then, the PCA (Principal Component Analysis) dimension reduction is carried out to the original electroencephalograph signals corresponding to the feature space, and the electroencephalograph signals with categorizing advantages are reconstructed; and finally, a linear discriminant function categorizer is selected to for categorizing. According to the invention, during expression identification, only target features are extracted from the acquired electroencephalograph signals, then, the acquired electroencephalograph signals are categorized, and the identification result can be determined; and the identification of the electroencephalograph signals can be realized based on the character expression stimulation. In the invention, the cognition of the human being is introduced, and the advantages of objectiveness and high efficiency are provided.

Description

technical field [0001] The invention relates to a method for processing and analyzing electroencephalogram signals, in particular to an electroencephalogram signal classification system and method based on effective time series and electrode reorganization. Background technique [0002] In today's society with increasingly close interpersonal relationships, it is of great survival significance to correctly recognize other people's expressions. This not only enables people to adjust their behavior in time to adapt to the environment, but also effectively avoids unnecessary dangers and is conducive to social interaction and environmental adaptation. At the same time, research on normal people can also provide references for clinical diagnosis and treatment, and can be used for prevention and treatment. At present, the main application fields of facial expression recognition technology include human-computer interaction, security, robot manufacturing, medical treatment, commun...

AI Technical Summary

Problems solved by technology

[0003] In the literature on expression recognition, expressions are mainly judged by image expression recognition and speech signal analysis, but these traditional methods of expression evaluation are subjective and easily denied by others

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