Magnetoencephalogram decoding method based on image features

Abstract

The invention relates to the technical field of signal processing, in particular to a magnetoencephalogram decoding method based on image features. The method comprises the steps: giving a signal stimulation to the brain, obtaining the needed magnetoencephalogram data, carrying out the preprocessing of the magnetoencephalogram data, carrying out the spatial filtering of the magnetoencephalogram data, and obtaining the magnetoencephalogram data; the spatial filtering is used for reducing the dimension of the evoked potential, improving the signal-to-noise ratio of the evoked potential and further virtualizing the magnetoencephalogram data, the virtualized image is small in data volume during processing, the space-time dynamic process of magnetoencephalogram signals is considered in the formation of grids, that is, the spatial signals are arranged according to the time sequence, the classification precision is improved, and the classification efficiency is improved. Gist features are selected as image features of magnetoencephalogram decoding, due to the fact that the Gist features are biologically inspired features, a good effect is achieved in image classification, finally, a linear support vector machine classifier is adopted for classifying magnetoencephalogram signals, and the method solves the problem that in the prior art, precision is not high when magnetoencephalogram data are classified.

Description

technical field [0001] The present application relates to the technical field of signal processing, in particular to a magnetoencephalogram decoding method based on image features. Background technique [0002] Brain Decoding is a data analysis method based on concurrent neuroimaging data to predict a subject's mental state. The researchers provided the subjects with stimuli, such as visual cues, and recorded the associated brain activity from multiple sensors. The recorded data, along with the type of stimulus, is called an experiment. Many small experiments collected in one experiment. In the brain decoding analysis stage, a classifier is constructed using part of the data to predict the function of brain activity stimuli. The rest of the data is used to estimate the accuracy of the classifier. Classification with high accuracy is considered evidence of stimulus-related information in the data, which reveals the relationship between the mental process of interest and i...

AI Technical Summary

Problems solved by technology

[0005] This application provides a magnetoencephalogram decoding method based on image features to solve the problem of low classification accuracy of brain decoding

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