Motor imagery brain-computer interface based on Bayesian network structure identification
A Bayesian network and motor imagery technology, applied in the field of medical equipment, can solve problems such as affecting the communication and control between patients and the outside world, reducing the accuracy of motor imagery EEG classification and identification, etc., to expand the classification feature set and improve the accuracy. Effect
Active Publication Date: 2020-10-02
INNER MONGOLIA UNIV OF TECH
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Problems solved by technology
[0004] However, in the existing BCI technology, only the EEG signal features of the same brain region are extracted, which will reduce the accuracy of motor imagery EEG classification and identification, and affect the patient's communication and control with the outside world
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[0033] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.
[0034] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and not necessarily Used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such th...
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The invention discloses a motor imagery brain-computer interface based on Bayesian network structure identification, and the motor imagery brain-computer interface comprises: a signal collection module which is configured to be used for collecting an electroencephalogram signal generated by brain activities; the signal processing module that is configured to extract features of the electroencephalogram signals according to a pre-constructed Bayesian network to obtain feature vectors, and classify the feature vectors; and the control equipment module that is configured to convert the classifiedfeature vectors into control instructions of external equipment and output the control instructions. According to the embodiment of the invention, the electroencephalogram signal modes are classified, the Bayesian network modeling method is introduced, the network information flow action intensity and directions of different brain intervals are extracted to serve as classification features, and due to the fact that the causal action relation of the different brain intervals of the brain network is introduced, a classification feature set is expanded, and the accuracy of motor imagery electroencephalogram classification and identification is remarkably improved.
Description
technical field [0001] The present invention generally relates to the technical field of medical devices, in particular to a brain-computer interface for motor imagery based on Bayesian network structure identification. Background technique [0002] In modern society, many diseases such as stroke, spinal cord injury and amyotrophic lateral sclerosis can reduce or impair the transmission function of the nervous system and the ability to control muscles. When the disease progresses to a severe level, patients may lose the ability to speak or control their own bodies. Therefore, how to help patients achieve self-care in life and realize their purpose of communicating with the outside world is an urgent problem to be solved. [0003] At present, there are two ways, one is to start from the main body of human body movement, with the help of peripheral nerves and muscle tissue, to control rehabilitation aids such as prosthetics and wheelchairs, to realize the missing motor functi...
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Login to View More IPC IPC(8): G06F3/01G06N7/00G06K9/62
CPCG06F3/015G06F2203/011G06N7/01G06F18/24155G06F18/29Y02D10/00
Inventor 董朝轶贾婷婷陈晓艳任婧雯
Owner INNER MONGOLIA UNIV OF TECH