Electroencephalogram signal classification method based on graph semi-supervised width learning

Abstract

The invention relates to a classification method based on graph semi-supervised width learning. According to the method, the labels of the labeled samples are extended to the unlabeled samples througha graph extension method, so that the labeled samples and the unlabeled samples are sent to the classifier for training, and a semi-supervised algorithm is realized. According to the method, firstly,a graph based on similarity between sample data is constructed, and meanwhile, an inter-sample difference regular term is added into composition, so that the constructed graph for label expansion ismore accurate; then the labeled and unlabeled samples are sent to a classifier to be trained, a semi-supervised classifier model is obtained, optimization solution is carried out, a weight matrix froman input layer to an output layer is mainly obtained, and thus corresponding labels can be obtained from the input samples through the weight matrix when the test set is tested. The method has a wideapplication prospect in electroencephalogram signal processing and brain-computer interface systems.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for classifying electroencephalogram signals using graph label extension and combining a small number of labeled samples for semi-supervised width learning. Background technique [0002] Brain-computer interface (BCI) is a technology that only needs to use the signals generated by the human brain when it receives specific stimuli to control external devices or systems, without relying on normal peripheral neuromuscular channels. In recent years, the application of BCI technology has become increasingly widespread, and has achieved fruitful results in fields such as games and aerospace. In the field of biomedical active rehabilitation, BCI is mainly used to accurately detect the patient's movement intention, so that the patient can actively participate in the exercise training process and induce neuroplasticity. The electroencephalogram (EEG) is the most widely used sign...

AI Technical Summary

Problems solved by technology

[0004] However, deep learning requires complex structural adjustments and a large amount of calculation during training. In this regard, Professor Chen Junlong of the University of Macau proposed a breadth learning algorithm (BLS). The essence of BLS is a random vector function link neural network.

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