EEG classification model generation method, device and electronic equipment

Abstract

The present application provides a method for generating an EEG classification model, a device for generating an EEG classification model, electronic equipment, and a computer-readable storage medium. The method for generating an EEG classification model includes: obtaining sample data of K subjects, wherein, The sample data includes: classified EEG information and classification results of the corresponding EEG information, the K is greater than or equal to 2; based on the sample data of K subjects and the preset first objective function, the calculation makes the first An orthogonal transformation matrix with the minimum value of the objective function, wherein the first objective function is a function related to the orthogonal transformation matrix and the EEG information of K test subjects, and the orthogonal transformation matrix is ​​used to transform the K test subjects The respective EEG information is transformed into correlation information among K subjects; an EEG classification model is generated based on the orthogonal transformation matrix. The technical solution of the present application is used to generate an EEG classification model that can be applied to multiple subjects, saving the maintenance cost of the EEG classification model.

Description

technical field [0001] The present application belongs to the technical field of brain-computer interaction, and in particular relates to a method for generating an EEG classification model, a device for generating an EEG classification model, electronic equipment, and a computer-readable storage medium. Background technique [0002] Electroencephalogram (EEG) is the overall reflection of the electrophysiological activity of brain nerve cells on the surface of the cerebral cortex or scalp. [0003] At present, brain-computer interaction technology based on EEG signals has become a research hotspot in the industry. The key technology of brain-computer interaction technology is how to quickly and effectively extract EEG information and improve recognition accuracy. Considering that EEG signals are highly non-stationary and individual differences, there are significant differences in the EEG classification models trained based on the EEG information of different subjects. Ther...

AI Technical Summary

Problems solved by technology

Since the EEG classification model can only be applied to one subject, the generalization performance of the existing brain-computer interaction system is poor, and, with the increase of the number of subjects, the brain-computer interaction system needs to maintain There are more and more EEG classification models, correspondingly, the maintenance cost of EEG classification models also increases

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