Lp norm iterative sparse solution for EEG source Localization

The azimuth projection for the display of 3-D EEG data

Electroencephalogram (EEG) is a scalp record of the neural electric activities of the brain. There are many kinds of methods to display the EEG data, such as a projective plane or the realistic head surface. In this work, one of the atlas projection methods, azimuth conformal projection, was tested and recommended as a new way of a planar EEG display. The method details are given and numerically compared with the normal projective plane display. The results indicate that the azimuth projection has many advantages: the transform is simple, convenient, and it can keep all the information. It shows all the information in the 3-D data within a projective plane without distinct shape change. Therefore, it can help to analyze the data effectively.

Evaluation of smoothing in an iterative lp-norm minimization algorithm for surface-based source localization of MEG

The imaging of neural sources of magnetoencephalographic data based on distributed source models requires additional constraints on the source distribution in order to overcome ill-posedness and obtain a plausible solution. The minimum l(p) norm (0 < p < or = 1) constraint is known to be appropriate for reconstructing focal sources distributed in several regions. A well-known recursive method for solving the l(p)-norm minimization problem, for example, is the focal underdetermined system solver (FOCUSS). However, this iterative algorithm tends to give spurious sources when the noise level is high. In this study, we present an algorithm to incorporate a smoothing technique into the FOCUSS algorithm and test different smoothing kernels in a surface-based cortical source space. Simulations with cortical source patches assumed in auditory areas show that the incorporation of the smoothing procedure improves the performance of the FOCUSS algorithm, and that using the geodesic distance for constructing a smoothing kernel is a better choice than using the Euclidean one, particularly when employing a cortical source space. We also apply these methods to a real data set obtained from an auditory experiment and illustrate their applicability to realistic data by presenting the reconstructed source images localized in the superior temporal gyrus.