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Human neuroimaging studies mostly combine data from many subjects to infer general patterns of brain activity shared between people. However, certain activity characteristics of the human brain have a high degree of inter-individual variability and intra-individual (cross-state) stability, which can be used as an individual's identification index (i.e. neural fingerprint). Extracting brain activity data through fMRI technology can analyse the neural fingerprint of each person, and realize the transition from group-level research to single-body research, so as to accurately identify a single subject from a large group. In this study, functional magnetic resonance imaging (fMRI) technology was used to record the brain activity data of two narrators and multiple subjects when they were telling/listening to stories. Researching and extracting brain activity data can analyse each person’s "neural fingerprints", which will ultimately be used in the study of brain pathology, and even improve diseases such as Alzheimer's disease and autism. Using independent component analysis (ICA), the 50 independent components in the extracted brain fMRI data are used as the nodes of the network, and the correlation coefficients between the time series of the independent components are used as the connecting edges to construct the functional connection network. In this paper, the functional connection matrix is used as a kind of "neural fingerprint" to identify and match individuals. The functional connection matrix obtained by using the data of the unknown subject's state is used as the target matrix, and the functional connection matrix obtained by using the data of the known subject's state as the database matrix. Compare the functional connection matrix of the target set with each functional connection matrix in the database to find the most similar matrix. Similarity is defined as the Pearson correlation coefficient between the target matrix and the vector obtained by vectorising the upper triangular elements of the data set matrix. The correlation coefficient corresponding to the identified identity is the maximum value among all correlation coefficients, and the ID that the data in the target set should match can be obtained from the maximum value.
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