Non-parametric statistical thresholding of baseline free MEG beamformer images

Magnetoencephalography (MEG) provides excellent temporal resolution when examining cortical activity in humans. Inverse methods such as beamforming (a spatial filtering approach) provide the means by which activity at cortical locations can be estimated. To date, the majority of work in this field has been based upon power changes between active and baseline conditions. Recent work, however, has focused upon other properties of the time series data reconstructed by these methods. One such metric, the Source Stability Index (SSI), relates to the consistency of the time series calculated only over an active period without the use of a baseline condition. In this paper we apply non-parametric statistics to SSI volumetric maps of simulation, auditory and somatosensory data in order to provide a robust and principled method of statistical inference in the absence of a baseline condition.