The invention discloses a method for extracting space-time feature vectors of individuals and finely classifying subjects, and belongs to the field of biomedical signal processing. According to the method, a high-dimensional coupling relation contained in a kernel tensor obtained through Tucker decomposition of multi-subject fMRI data in the form of 'space voxel * time * subject' is fully mined, aiming at the kernel tensor obtained through a space sparse constraint Tucker decomposition method, an extraction method of space-time feature vectors of each subject is provided, and the space-time feature vectors are used for k-means subject fine classification. In individual space-time feature vector extraction and subject fine classification of task state fMRI data of ten healthy subjects, according to a spatial feature matrix corresponding to DMN components, all subjects are divided into two groups, and the average activated voxel number of an IPL region in spatial activation of each subject in the group 1 is 109% more than that in the group 2; all subjects are divided into two groups according to the time characteristic matrix corresponding to the task related components, and the average correlation coefficient of the task state time process of the group 1 and the reference components is 55.6% higher than that of the group 2. The individual space-time differences can provide a new objective basis for brain function research and brain disease diagnosis.
Method for extracting space-time feature vectors of individuals and finely classifying subjects