Low-Quality Magnetoencephalography Data Reconstruction

Magnetoencephalography is a brain imaging method with high temporal-spatial resolution, whose data quality is reduced due to the failure of sensors. This study aimed to reconstruct the low-quality data of magnetoencephalography signals using surface reconstruction methods, partial differential equations algorithms, and finite element-based methods. To evaluate the performance of each method, R-square, root mean square error, and signal-to-noise ratio were calculated. The relation between these criteria was checked through proper statistical tests with a significance level of 0.05. The median method with mean and variance of R-square equal to 0.87±0.03 was better than the other methods.