The invention discloses a multi-subject fMRI data Tucker decomposition method introducing a spatial sparse constraint, and belongs to the field of biomedical signal processing. The Spatial sparse constraint is introduced into an RKCA low-rank constraint Tucker decomposition model, the problem of high noise of multi-subject fMRI data in the form of space voxel * time * subject is solved, sparsity of multi-subject shared space components is matched, and the multi-subject shared space components and shared time components and the kernel tensor contains rich tested individual information are effectively extracted from the multi-subject fMRI data. In fMRI data decomposition of ten healthy testee task states, compared with an RKCA method, correlation coefficients of shared space-time components and reference components of the extracted task states and default networks are increased by 58.3% and 29.6% respectively, correlation coefficients of shared space-time components and reference components of the extracted task states and default networks are increased by 31.7% and 31.9% respectively, the number of activated voxels in space reference is increased by 51.1% and 26.0% respectively, and a new method is provided for brain cognition and brain disease research.
Multi-subject fMRI data Tucker decomposition method introducing space sparse constraint