Sparse Source Imaging in EEG

We have developed a new L1-norm based minimum norm estimate (MNE), which is termed as sparse source imaging (SSI). The new SSI algorithm corrects inaccurate orientation discrepancy in previously reported L1-norm MNEs. A new solver to the newly developed SSI has been adopted and known as the second order cone programming (SOCP). The new SSI is assessed by a series of computer simulations. The performance of SSI is compared with other L1-norm MNEs by evaluating the localization error and orientation error. The present simulation results indicate that the new SSI has significantly improved performance, especially in the metric of orientation error. The previously reported L1-norm MNEs show large orientation errors due to the orientation discrepancy. The new SSI algorithm is also applicable to MEG source imaging.