Electroencephalogram signal classification model training method and device, and electronic equipment

Abstract

The invention provides an electroencephalogram signal classification model training method and device, and electronic equipment. The training method comprises the following steps: acquiring an electroencephalogram signal sample set, wherein the electroencephalogram signal sample set comprises a plurality of sample subsets corresponding to a plurality of categories respectively; for each sample subset in the multiple sample subsets, based on time-frequency domain transformation and sample superposition, generating a superposed sample set corresponding to the sample subset by utilizing each sample in the sample subset; for each sample subset in the multiple sample subsets, generating an amplified sample subset of the sample subset by utilizing the sample subset and the corresponding superposed sample set; and training a classification model for electroencephalogram signal classification by using the multiple amplified sample subsets corresponding to the multiple categories respectively.The trained classification model is used for classifying the acquired to-be-classified electroencephalogram signals into one of the multiple categories.

Description

technical field [0001] The present application relates to the medical field, and more specifically, to a method and device for training a classification model for classification of electroencephalogram signals, and electronic equipment. Background technique [0002] Brain-computer interface (BCI) is widely used in medical scenarios. It is a technology that directly communicates between the brain and external devices such as computers without relying on the normal output pathways of the brain (peripheral nerves and muscle tissue). For example, for a stroke patient with damage to the sensorimotor cortex, a brain-computer interface (BCI) can collect signals from the damaged cortical area, and then stimulate muscles or control orthotics to improve arm movement. The brain of epilepsy patients will have abnormal discharge of neurons in a certain area. After the abnormal discharge of neurons is detected through brain-computer interface technology, corresponding electrical stimulati...

AI Technical Summary

Problems solved by technology

However, the deep learning model requires a large amount of data for training, and the small amount of training data makes it difficult to train a high generalization model for the classification task of EEG signals

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