Motor imagery brain-computer interface control method based on noninvasive electrical stimulation

Abstract

The invention provides a motor imagery brain-computer interface control method based on noninvasive electrical stimulation. The control method comprises the following steps: applying anode noninvasive brain function electrical stimulation on the primary motor cortex of a user; collecting an electroencephalography original signal after the user executes a motor imagery task, and extracting an event-related desynchronization signal and / or event-related synchronization signal from the electroencephalography original signal; carrying out quantification processing on the event-related desynchronization signal and / or event-related synchronization signal to obtain the average power of the event-related desynchronization signal and / or event-related synchronization signal in a Lower[Mu] waveband, an Upper[Mu] waveband and a [Beta] waveband; carrying out feature extraction on the event-related desynchronization signal and / or event-related synchronization signal to obtain the feature values of the event-related desynchronization signal and / or event-related synchronization signal; and according to the average power, classifying the feature values to obtain a control instruction, wherein the control instruction is used for controlling a feedback training normal form.

Description

technical field [0001] The invention relates to the technical field of brain-computer interface control, in particular to a motor imagery brain-computer interface control method based on non-invasive electrical stimulation. Background technique [0002] Brain-Computer Interface (BCI) is a system that can transmit information to the world outside the brain and provide alternative pathways. It is widely used in sports training, communication, entertainment and other fields. At present, the most mature BCI system is mainly based on the EEG (Electroencephalography, EEG) BCI system. This BCI system collects the EEG signal of the scalp, and extracts and classifies the features of this signal to extract the brain activity intention. signal, so as to achieve the purpose of control and communication. [0003] BCI based on motor imagery (Motor Imagery, MI) means that users spontaneously adjust their own sensorimotor rhythm (Sensorimotor Rhythm, SMR) by imagining a certain movement pr...

AI Technical Summary

Problems solved by technology

Moreover, as wearable devices become more and more popular, small size, light weight, and low power consumption have become an inevitable trend in the development of hardware devices. It is not a small challenge to run complex algorithms on these devices. Therefore, the above methods are practical. The effect is not obvious in the application

[0007] Second, from the user's point of view, the user's adaptation to the motor imagery BCI system requires repeated feedback training. Although this is generally imagined, it does cause a burden to the user and make the user feel tired and uncomfortable

Because no matter how accurate the classification algorithm is, it cannot improve the user's ability to adjust SMR spontaneously

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