Abstract: Abstract: Functional magnetic resonance imaging (FMRI) is a medical imaging technology widely employed in brain region positioning for its non-invasiveness and high spatiotemporal resolution. However, the traditional FMRI signal separation algorithm was too complex and time-consuming to effectively apply the FMRI technology to brain function research. Aiming at the computational complexity of traditional FMRI brain separation algorithms, a blind separation algorithm was proposed based on the second-order Hadamard transform. This algorithm first calculated the correlation function of the blood oxygen level dependent (BOLD) signal in the fMRI data, and then performed eigenvalue decomposition to obtain the unmixing matrix, thereby realizing the activation of brain regions. Given the composition of the Hadamard being only 1 or 1, the complexity can be reduced for the BOLD signal correlation matrix calculation. The simulation results show that compared with the independent component analysis (ICA) of high-order statistics and the Fourier transform blind separation algorithm of second-order statistics, the calculation time of this algorithm was only 25% and 50% of theirs, respectively, while the positioning error was close.

Key words: Keywords: functional magnetic resonance imaging, blind separation, independent components analysis, second order blind identifiability, brain activation area