Brain cognitive state judgment method based on polyteny principal component analysis

Abstract

The invention discloses a brain cognitive state judgment method based on polyteny principal component analysis (PCA). The method includes the following steps of firstly, inputting sample sets, and processing input data; secondly, calculating characteristic decomposition of training sample sets, determining an optimal feature transformation transformational matrix, and projecting training samples into tensor characteristic subspace to obtain feature tensor sets of the training sets; thirdly, vectorizing lower dimension feature tensor data which are subjected to dimensionality reduction as input of linear discriminant analysis (LDA), determining an LDA optimal projection matrix, and projecting the vectorized lower dimension feature tensor data into LDA feature subspace for further extracting discriminant feature vectors of the training sets; and fourthly, classifying features, subjecting the discriminant feature vectors obtained by projection of training images and test images to feature matching, and further classifying the features . According to the brain cognitive state judgment method, PCA is utilized to directly perform dimensionality reduction and feature extraction to multi-level tensor data, the defect that structures and correlation of original image data are destroyed and redundancy and structures in the original images can not be completely maintained due to the fact that traditional PCA simply performs dimensionality reduction is overcome, and space structure information of functional magnetic resonance image (fMRI) imaging data is kept.

Description

technical field [0001] The invention belongs to the field of biological feature extraction and brain cognitive state determination and classification, and relates to the preprocessing of functional nuclear magnetic imaging of cerebral blood oxygen level, extraction of multi-linear principal components and linear discriminant classification, and is a feature extraction algorithm based on multi-linear principal component analysis and linear discriminant classification algorithms. Background technique [0002] Cognitive neuroscience is a discipline developed on the basis of cognitive science and neuroscience, and its core subdisciplines are cognitive psychology and artificial intelligence. Among them, cognitive psychology combines information processing theory to explain human cognitive process, and adopts scientific methods to carry out experimental research on human perception, attention, memory, language and other cognitive processes. At the same time, the development of ar...

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Problems solved by technology

[0008] PCA only vectorizes image data and extracts features based on eigenvalues ​​and eigenvectors, ignoring the factor that tensor objects are often multi-level data, resulting in poor dimensionality reduction effects caused by only projecting in one direction. Ideal and destroys the structure and correlation of the original image, and cannot fully maintain the redundancy and structure in the original image

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