Abstract
In MEG experiments an electromagnetic field is measured at a very high temporal resolution in many sensors located in a helmet-shaped dewar, producing a very large dataset. Filtering techniques are commonly used to reduce the noise in the data. In this paper, spatiotemporal smoothing across space and time simultaneously is used, not simply as a pre-processing step, but as the central focus of a modelling technique intended to estimate the structure of the spatial and temporal response to stimulus. A particular advantage of this approach is the ability to study responses from individual replicates, rather than averages. The benefits of this form of smoothing are discussed and simulation used to evaluate its performance. The methods are illustrated on an application with real data.
