Method for measuring brain cognition on basis of deep learning feature extraction and multiple-dimensionality reduction algorithms

Abstract

The invention belongs to the field of brain cognitive ability measurement, and particularly relates to a method for measuring brain cognition on the basis of deep learning feature extraction and multiple-dimensionality reduction algorithms. High-dimensionality brain image features can be intelligently, conveniently and quickly extracted by the aid of the method, and the cognitive ability can be intelligently, conveniently and quickly measured by the aid of the method. The method includes extracting features of multiple channels for inputted brain image data by feature extraction networks and acquiring a local feature vector by means of straightening and splicing operation; carrying out orthogonal projection dimensionality reduction on the acquired local feature vector; measuring the cognitive ability for dimensionality-reduced local features on the basis of preliminarily constructed cognitive ability-local feature corresponding relations and outputting measurement results. The method has the advantages that the brain cognitive ability can be automatically, intelligently, conveniently and quickly measured by the aid of the method; the method is high in recognition accuracy.

Description

technical field [0001] The invention belongs to the field of brain cognitive ability measurement, and in particular relates to a brain cognitive measurement method based on deep learning feature extraction and multiple dimensionality reduction algorithms. Background technique [0002] At present, the way to measure the cognitive ability of the human brain is mainly the way of questionnaire measurement, such as examination and test, questionnaire survey and so on. This method is usually interfered by supervisory factors, including the emotional or mental state of the main tester or the testee, so it is usually difficult to obtain stable and objective evaluation results. With the advancement of brain neuroimaging technology, it has been possible to measure the structure and functional activity information of the brain in the form of high spatial and temporal resolution. However, this three-dimensional brain imaging data contains a large number of features of brain structure a...

AI Technical Summary

Problems solved by technology

However, this three-dimensional brain imaging data contains a large number of features of brain structure and function. How to use these features to measure the cognitive ability of the human brain poses a huge challenge to conventional feature-based machine learning methods: first , different from object recognition in conventional computer vision (such as recognizing object shape categories, etc.), it is impossible for humans to clearly define which features are valid features on brain imaging, especially for functional neuroimaging, humans (including doctors) cannot judge the value of features; Secondly, since 3D brain images contain a large amount of data for individuals, the screening of features becomes particularly critical.

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