User characteristic model establishment method and system based on brain-computer interface and storage medium

Abstract

The invention discloses a user characteristic model establishment method and system based on a brain-computer interface and a storage medium. The method comprises the steps that motor imagery electroencephalogram signals are acquired, and acquired motor imagery electroencephalogram signals are preprocessed; Fourier transformation is conducted on the preprocessed motor imagery electroencephalogramsignals to obtain frequency spectrum, meanwhile Hilbert-Huang transformation is conducted on the preprocessed motor imagery electroencephalogram signals to obtain instantaneous amplitude and instantaneous phase, and feature extraction is conducted on the frequency spectrum, the instantaneous amplitude and the instantaneous phase; a genetic algorithm is utilized to conduct feather screening on extracted features, and the screened features are utilized to train a classifier; the trained classifier serves as a user feature model output. Accordingly, the accuracy rate and stability of a model areimproved. Different users are large in electroencephalogram signal difference, and a model having optimum electroencephalogram signal features is determined for a specific user.

Description

technical field [0001] The present invention relates to the technical field of brain-computer interface, in particular to a method, system and storage medium for establishing a user characteristic model based on a brain-computer interface. Background technique [0002] Brain-computer interface (brain-computer interface, BCI) is a direct communication channel established between the human brain and external devices. Through this channel, people can directly issue commands to external devices through the brain without language or action. Support, can effectively enhance the ability of users to communicate with the outside world or control the external environment, thereby improving the quality of life of patients. In view of its huge application prospects, the brain-computer interface has attracted great attention from the international scientific community, and has become an important field in brain science, rehabilitation engineering, It is a research hotspot in the field of...

AI Technical Summary

Problems solved by technology

Due to the complexity of EEG signals, studies have shown that different users have great differences in the characteristics of EEG signals, and the fixed feature extraction model cannot reflect the individual differences of users, thus reducing the classification accuracy and accuracy of the brain-computer interface system.

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