Motion intention prediction method based on brain network dynamic connection characteristics
A technology of motion intention and dynamic connection, applied in the field of EEG signal analysis, can solve problems such as ignoring the dynamic connection of EEG
Pending Publication Date: 2020-07-28
SOUTHEAST UNIV
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Although time-domain or frequency-domain features of motor-related cortical potentials have been proven to be successful in predicting motor intentions, these methods have ignored the dynamic connections of EEG in different brain regions over time.
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[0028] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0029] The present invention designs a method for predicting motion intention based on the dynamic connection characteristics of the brain network, such as figure 1 As shown, the steps are as follows:
[0030] Step 1: Construct a time-varying dynamic Bayesian network model (TV-DBN), decompose the directed weighted connection matrix in the model according to the two orthogonal coordinate axes of time and electrode, and transform it into L1 regularized weighted least squares Regression problem, solve this problem, thereby transform the EEG signal matrix in the time domain into the directed weighted connection matrix between the electrodes in the time domain, and each element in the described directed weighted connection matrix corresponds to the connection between the two electrodes at the current moment connection coefficient;
[0031] Ste...
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The invention discloses a motion intention prediction method based on brain network dynamic connection characteristics. The motion intention prediction method comprises the following steps of constructing a time-varying dynamic Bayesian network model, and converting an EEG signal matrix of a time domain into a directed weighted connection matrix between electrodes of the time domain; carrying outpairing t test on each electrode pair by the obtained directed weighted connection matrix according to a defined motion intention state and a rest state, screening to obtain electrode pairs with significant difference in two states, and reconstructing a feature matrix according to the electrode pairs obtained by screening and time; and reconstructing the obtained feature matrix into a feature vector, inputting the feature vector into the trained classifier, and outputting a prediction result of the motion intention. The defect that a traditional electroencephalogram motion intention predictionmethod ignores the dynamic relation between different areas of the cerebral cortex is overcome.
Description
technical field [0001] The invention belongs to the field of EEG signal analysis, and in particular relates to a method for predicting motion intention. Background technique [0002] Generally speaking, human body activities are controlled by the brain's will, so in order to assist and restore the motor communication ability of patients with impaired motor abilities such as disability and paralysis, it is crucial to predict the patient's motor intentions through the brain-computer interface. For the prediction of motor intentions, most of the published studies are based on the time-domain or frequency-domain features of movement-related cortical potentials. Although time-domain or frequency-domain features of motor-related cortical potentials have been proven to successfully predict motor intentions, these methods ignore the dynamic connections of EEG in different brain regions over time. In order to take into account the dynamic connections between different regions of the...
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IPC IPC(8): A61B5/0476A61B5/00
CPCA61B5/7225A61B5/7235A61B5/7267A61B5/7203A61B5/7275A61B5/369
Inventor 曾洪刘兴黄孝妍沈俊杰王新志宋爱国
Owner SOUTHEAST UNIV