The dynamic beamformer

Abstract

As the number of possible neural sources is much higher than the number of MEG or EEG sensor readings, the inverse problem of estimating source amplitudes from sensor readings has many solutions. A common approach to tackle this problem is to assume that all sources are independent from each other. This approach is widely used in the neuroscience community and is known as beamforming. Since the source amplitude is likely to change smoothly over time, we expect to improve the source localization by taking the temporal dynamics into account. In this paper, we incorporate the independence assumption of the standard beamformer in a linear dynamic system, and we show that by using the leadfield matrix as the observation model and setting the covariance of the observation noise to be proportional to the covariance of the observation, we arrive at the dynamic beamformer