Electroencephalogram signal classification method based on model uncertainty learning

Abstract

The invention discloses an electroencephalogram signal classification method based on model uncertainty learning, and the method comprises the steps: 1, carrying out the preprocessing of original electroencephalogram data, including data selection, sliding window slicing, data upsampling at the early stage of attack, and data input shape selection; 2, establishing a deep learning model of the Pelee network; 3, embedding a dropout layer at the tail part of the model; 4, in a training stage, inputting data and continuously optimizing model parameters through cross entropy loss to obtain a final classification model for classification of electroencephalogram signals to be tested; and 5, in a test stage, in combination with an improved Monte Carlo discarding sampling technology based on continuous sample time information aggregation, performing prediction classification on electroencephalogram samples to be classified. According to the method, the model uncertainty is combined into a lightweight network (PeleeNet), so that the classification accuracy of the electroencephalogram signals can be remarkably improved, and the application value of the electroencephalogram signals in the fields of medical treatment and the like is increased.

Description

technical field [0001] The invention relates to the field of EEG signal classification, in particular to a method for predicting and classifying EEG signals with Pelee network combined with uncertainty learning technology. Background technique [0002] Electroencephalography (EEG) is a physiological technique used to record electrical activity in the brain. Identifying and predicting physiological and psychological states from neural activity patterns observed in scalp and intracranial EEG is widely used in brain-computer interface fields such as emotion recognition, motor imagery, and medical health. Linear or nonlinear features, such as autoregressive coefficients and Lyapunot exponents, are manually extracted using traditional machine learning methods that have achieved moderate success in tightly controlled experimental settings. However, these manually extracted features often require researchers with extensive expertise and extensive experimentation. Furthermore, in ...

AI Technical Summary

Problems solved by technology

However, the classification of EEG states has been challenged by insufficient training data due to the high cost of data acquisition.

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