title: Array-gain constraint minimum-norm spatial filter with recursively updated gram matrix for biomagnetic source imaging.
abstract: 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.
authors.affiliation: Department of Systems Design and Engineering, Tokyo Metropolitan University, Tokyo 191-0065, Japan. kumihashi136_ac@yahoo.co.jp
authors.forename: Isamu
authors.initials: I
authors.lastname: Kumihashi
authors.forename: Kensuke
authors.initials: K
authors.lastname: Sekihara
mesh_terms: D000465:Algorithms; D001303:Auditory Cortex / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted