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It is generally believed that because the skull has low conductivity to electric current but is transparent to magnetic fields, the measurement sensitivity of the magnetoencephalography (MEG) in the brain region should be more concentrated than that of the electroencephalography (EEG). It is also believed that the information recorded by these techniques is very different. If this were indeed the case, it might be possible to justify the cost of MEG instrumentation which is at least 25 times higher than that of EEG instrumentation. The localization of measurement sensitivity using these techniques was evaluated quantitatively in an inhomogeneous spherical head model using a new concept called half-sensitivity volume (HSV). It is shown that the planar gradiometer has a far smaller HSV than the axial gradiometer. However, using the EEG it is possible to achieve even smaller HSV's than with whole-head planar gradiometer MEG devices. The micro-superconducting quantum interference device (SQUID) MEG device does have HSV's comparable to those of the EEG. The sensitivity distribution of planar gradiometers, however, closely resembles that of dipolar EEG leads and, therefore, the MEG and EEG record the electric activity of the brain in a very similar way.

We examined how the cerebrospinal fluid (CSF) affects the distribution of electroencephalogram (EEG) measurement sensitivity. We used concentric spheres and realistic head models to investigate the difference between computed-tomography (CT) and magnetic resonance image (MRI) models that exclude the CSF layer. The cortical EEG sensitivity distributions support these phenomena and show that the CSF layer significantly influences them, thus identifying the importance of including the CSF layer inside the head volume conductor models. The results show that the highly conductive CSF channels the current, thus decreasing the maximum cortical current density relative to models that do not include the CSF. We found that the MRI and CT models yielded HSV results 20% and 45%, respectively, too small when compared with CSF-inclusive models.

We present the four key areas of research—preprocessing, the volume conductor, the forward problem, and the inverse problem—that affect the performance of EEG and MEG source imaging. In each key area we identify prominent approaches and methodologies that have open issues warranting further investigation within the community, challenges associated with certain techniques, and algorithms necessitating clarification of their implications. More than providing definitive answers we aim to identify important open issues in the quest of source localization.

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