Brain cognitive state judgment method based on non-negative tensor projection operator decomposition algorithm

Abstract

Disclosed is a brain cognitive state judgment method based on a non-negative tensor projection operator decomposition algorithm. The method includes the steps of S1, collecting brain functional magnetic resonance images under different cognitive tasks to form a data sample set, carrying out preprocessing, and forming a sample set according to tensor modes, wherein the sample set is divided into a training set and a testing set according to the cognitive tasks, and the training set comprises functional magnetic resonance data in similar proportion of different cognitive states, S2, computing non-negative tensor projection operator decomposition of the training sample set to solve out a non-negative feature transformation matrix, projecting training samples to a non-negative tensor feature sub-space for dimensionality reduction to obtain a non-negative feature tensor set of the training set, S3, using lower-dimension non-negative feature tensor data after dimensionality reduction as input of an STM for training to solve out the optimum projection direction of the STM, and S4, projecting brain functional magnetic resonance data of tested samples to the non-negative tensor feature sub-space obtained through training to obtain non-negative feature tensors of the brain functional magnetic resonance data in the sub-space, and inputting the non-negative feature tensors of the tested samples to the trained STM to judge cognitive state types of the non-negative feature tensors.

Description

technical field [0001] The invention belongs to the field of brain functional magnetic resonance image feature extraction and brain cognitive state classification, and relates to the preprocessing of functional nuclear magnetic imaging data of cerebral blood oxygen level and its tensor mode expression, and the non-negative function of brain functional magnetic resonance data based on tensor mode. Dimensionality reduction and application support tensor machine discriminant classification, which is a feature extraction algorithm and discriminative classification algorithm based on tensor patterns. Background technique [0002] For a long time, people have been making various efforts, hoping to unravel the mystery of brain cognitive function, but this research is still going on today, especially in recent years, with the development of brain functional imaging technology, cognitive neuroscience, computing The rapid development of neuroscience has formed a new upsurge in the stu...

AI Technical Summary

Problems solved by technology

This method directly processes tensor data, performs non-negative dimensionality reduction and feature extraction on tensor-mode fMRI data objects from multiple directions, and overcomes the traditional NMF's simple dimensionality reduction that destroys the original image data. structure and correlation, the lack of information and structure in the original image data cannot be fully preserved

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