Effect of the signal-to-noise ratio on the quality of linear estimation reconstructions of distributed current sources

Summary

Currently, linear estimation reconstruction is the only feasible method for extracting information about spatially distributed current sources from measurements of neural magnetic fields. We present the results of a systematic study of the effect of the signal-to-noise ratio on the imaging quality of one such algorithm in over- as well as underdetermined circumstances. In particular, we will discuss the necessary trade-off between the contradictory goals of a minimum norm of the reconstructed current density distribution and of a minimal deviation of the reconstructed fields from the measured fields. As an example, we show the reconstruction of a simple arrangement of two nearly parallel dipoles in two different depths inside a spherical volume conductor, discussing the differences between the computer simulation without noise and simulation with a realistic noise level.