Electroencephalogram feature extracting method based on brain function network adjacent matrix decomposition

Abstract

The invention relates to an electroencephalogram feature extracting method based on brain function network adjacent matrix decomposition. The current motion image electroencephalogram signal feature extraction algorithm mostly focuses on partially activating the qualitative and quantitative analysis of brain areas, and ignores the interrelation of the bran areas and the overall coordination. In light of a brain function network, and on the basis of complex brain network theory based on atlas analysis, the method comprises the steps of: firstly, establishing the brain function network through a multi-channel motion image electroencephalogram signal, secondly, carrying out singular value decomposition on the network adjacent matrix, thirdly, identifying a group of feature parameters based on the singular value obtained by the decomposition for showing the feature vector of the electroencephalogram signal, and fourthly, inputting the feature vector into a classifier of a supporting vector machine to complete the classification and identification of various motion image tasks. The method has a wide application prospect in the identification of a motion image task in the field of brain-machine interfaces.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal processing, and relates to an electroencephalogram feature extraction method, in particular to a feature extraction method for motor imagery electroencephalogram signals in a brain-computer interface. Background technique [0002] Brain-computer interface (BCI) is a direct communication pathway established between the brain and the outside world without the participation of peripheral nerves and muscle tissue. It can interpret brain signals into corresponding commands to achieve communication and communication with the outside world. control. Compared with detection techniques such as electrocorticography (ECoG), magnetoencephalography (EMG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET), electroencephalography (EEG) is relatively simple and fast, and is useful for humans. It is lossless, cheap, and has high time resolution, so it has become the mo...

AI Technical Summary

Problems solved by technology

However, most of them focus on the qualitative and quantitative analysis of locally activated brain regions, intentionally or unintentionally treating each brain region as an isolated functional unit, and ignoring the interrelationships and overall coordination between brain regions.

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