Array-gain constraint minimum-norm spatial filter with recursively updated gram matrix for biomagnetic source imaging.

This paper proposes a novel spatial filter for biomagnetic source imaging. The proposed spatial filter is derived based on a modified version of the minimum-norm spatial filter and is designed to have a performance close to that of the adaptive minimum-variance spatial filter through the use of an estimated covariance matrix. In this method, the theoretical form of the measurement covariance matrix is estimated as an updated gram matrix in a recursive procedure. Since the proposed method does not use the sample covariance matrix, it is free of the well-known weaknesses of the minimum-variance spatial filter, namely, the proposed spatial filter does not require a large number of time samples, and it can even be applied to single-time-sample data. It is also robust to source correlation. We have validated the method's effectiveness by our computer simulations as well as through experiments using auditory-evoked magnetoencephalographic data.