Semi-supervised EFL classification method based on graph balance regularization

Abstract

The invention relates to a semi-supervised ultra-limit learning machine classification method based on graph balance regularization. The present invention balances the adjacency graph based on label consistency and the adjacency graph based on information structure consistency through non-negative weights, so as to achieve graph balance, and can obtain the Laplacian regular term of the optimal graph to constrain the model, And it is considered that when the information consistency map cannot describe the structural information of the sample set well, the weight of the label consistency map should be increased, otherwise the corresponding proportion should be reduced. The present invention first constructs the adjacency supervised graph between the training samples through the label consistency of the samples, combines it with the semi-supervised graph based on the consistency of the sample information to constrain the output of the model, and changes the description data by reasonably adjusting the proportion of the graph The ability of the distribution, after obtaining the optimal adjacency graph, the best output weight vector is obtained. The invention has broad application prospects in brain-electrical signal processing and brain-computer interface systems.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and is a method for classifying EEG signals by constructing a Laplacian regular term using a graph based on sample label consistency and information consistency and adding it to a semi-supervised extreme learning machine. Background technique [0002] Brain-computer interface technology (BCI) is an important means to realize human-computer interaction through the analysis method of electroencephalogram signal (EEG), and the classification of EEG signal based on motor imagery is a typical paradigm in BCI technology. Actions are discriminated, and the results are converted into equipment control commands to complete the corresponding imaginary actions, which can provide great help for the rehabilitation of patients with disturbance of consciousness and stroke. Patients can use this technology to realize the control of mechanical equipment and complete the required tasks. Actions. In order to acc...

AI Technical Summary

Problems solved by technology

[0005] When constructing the sample adjacency graph, the machine learning algorithm based on graph regularization can only construct the adjacency graph by using the consistency of the sample label or the similarity of the sample's own structural information, but does not comprehensively consider the synergy between the two. Impact on the trained model

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