Simulation Study of Dipole Localization in MEG-Based BCI Using Magnetic Field Reconstruction

Brain-computer interface BCI based on magnetoencephalography MEG provides intentional signals with high spatial resolution for communications of patients suffering severe motor dysfunctions. However, the incomplete measurements of the extremely weak magnetic field outside head bring significant uncertainties to active neural source ANS localization. This paper presents a novel computational method to improve the ANS localization accuracy. Based on the fact that the external magnetic field obeys the Maxwell equations quasistatic, the field can be reconstructed via solving a Laplace's equation with measured boundary conditions. By numerically solving Laplace's equation with finite element method FEM, the signal-to-noise ratio of the reconstruction can be improved with high-order interference eliminated. The inverse estimation model, the reconstructions, and reconstruction selection are presented, and validated via simulation. Results show that about half of the dipole localization error is eliminated compared with method utilizing only measurements.

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