The following analysis path is probaby the quickest to lead you from your MEG data to presentable source estimates, with some compromised with respect to accuracy and statistical analysis. The general idea is this:

1. interpolate your MEG data to a standardised sensory array
2. apply statistics in "signal space" (e.g. using SensorSPM), in order to detect significant contrasts and latency ranges
3. run source analysis using a standardised head model on the grand-averaged MEG data
4. possibly apply the same source analysis on individual subject data, e.g. for statistical analysis in "source space"

This closely resembles the analysis strategy employed for ERP analysis, where electrodes are usually placed at standardised positions (e.g. the "10/20 system"), such that data for different subjects are very easy to combine (e.g. for grand-averages, statistics on the same eletrodes etc.).

In MEG recordings, the position and orientation of the sensor array usually differs significantly across subjects (unless special precautions are taken). Until recently, it was not straightforward to interpolate MEG data from different subjects into a common sensor array (e.g. the average across subjects). It has therefore been the standard to analyse MEG data only in source space, i.e. after source estimation procedures have been applied on single-subject data. This is the basis for some of the "fMRI-like" analysis strategies, as e.g. implemented in SPM5.

Although it is obviously the ultimate goal to make inferences about the timing AND localisation of neural generators from EEG/MEG data (for statistics or for real single-case studies), this approach also has some shortcomings:

1. Some source estimation procedures rely on rather restrictive modeling assumptions. This is the case for dipole models, for example, which make assumptions about the number and approximate locations of sources. These assumptions may not be fullfilled for single-subject data, either because of high noise levels, or because of inter-individual variation of the generator distributions.
2. Some distributed source methods make weaker assumptions about the number active sources, but still implement constraints on focality of sources (e.g. L1-norm, multiple sparse priors). These methods may still require a high SNR in order to produce accurate results, which may not be achieved for single-subject data.