Motor imagery electroencephalogram signal processing method and device, and storage medium

Abstract

The invention discloses a motor imagery electroencephalogram signal processing method and a device, and a storage medium. The method comprises the following steps: respectively inputting a source MI electroencephalogram signal belonging to a source domain and a target MI electroencephalogram signal belonging to a target domain into an initial feature extraction model to obtain a first source MI feature and a first target MI feature; inputting the first source MI feature into an initial classification model to obtain a first classification result output by the initial classification model, thefirst classification result is used for representing an action expected to be executed by the source MI electroencephalogram signal; under the condition that a first known action corresponding to thesource MI electroencephalogram signal is inconsistent with an action expected to be executed by the source MI electroencephalogram signal represented by the first classification result or the similarity between the feature distribution of the first source MI feature and the feature distribution of the first target MI feature is smaller than a first preset threshold value, determining that the first target MI feature is not consistent with the first target MI feature; and adjusting model parameters of the initial feature extraction model and / or model parameters of the initial classification model to obtain a target feature extraction model and a target classification model.

Description

technical field [0001] The invention relates to the field of computers, in particular to a method and device for processing motor imagery EEG signals and a storage medium. Background technique [0002] MI-BCI (Motor Imagery-Brain-Computer Interface, Motor Imagery-Brain-Computer Interface) is a human-computer interaction method that controls the movement of external devices by spontaneously imagining limb movements. The system based on MI-BCI can help patients with physical disabilities to carry out rehabilitation training, control machines to achieve self-care and improve the quality of life, and can also enrich the lives of ordinary people, such as brain-computer games. [0003] At present, the MI-BCI system separately trains a model for each subject's EEG signal, and uses the trained model to identify the subject's EEG signal. [0004] The performance of the MI-BCI system obtained by the above method is greatly affected by the decoding accuracy of MI EEG signals. Due to ...

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Problems solved by technology

[0006] Embodiments of the present invention provide a motor imagery EEG signal processing method, device, and storage medium to at least solve the problem in the related art that the MI recognition model is not accurate to the MI EEG of different subjects due to the large individual differences in the MI EEG signal. Technical problems of low decoding accuracy of electrical signals

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