Modeling the sensor array
The details of the sensor geometry and pick-up technology are dependent on the manufacturer of the array. We may however summarize some fundamental principles in the next following lines.
We have already reviewed how the sensor locations can be measured with state-of-the-art MEG and EEG equipment. If this information is missing, sensor locations may be roughly approximated from montage templates, but this will be detrimental to the accuracy of the source estimates (Schwartz, Poiseau, Lemoine, & Barillot, 1996). This is critical with MEG, as the subject is relatively free to position his/her head within the sensor array. Typical 10/20 EEG montages offer less degrees of freedom in that respect. Careful consideration of this geometrical registration issue using the solutions discussed above (HPI, head digitization and anatomical fiducials) should provide satisfactory performances in terms of accuracy and robustness.
In EEG, the geometry of electrodes is considered as point-like. Advanced electrode modeling should include the true shape of the sensor (that is, a ‘flat’ cylinder), but it is generally acknowledged that the spatial resolution of EEG measures is coarse enough to neglect this factor. One important piece of information however is the location of the reference electrode – i.e., nasion, central, linked mastoids, etc. – as it defines the physics of a given set of EEG measures. If this information is missing, the EEG data can be re-referenced with respect to the instantaneous arithmetic average potential (Niedermeyer & Silva, 2004).
In MEG, the sensing coils may also be considered point-like as a first approximation, though some analysis software packages include the exact sensor geometry in modeling. The computation of the total magnetic flux induction captured by the MEG sensors can be more accurately modeled by the geometric integration within their surface area. Gradiometer arrangements are readily modeled by applying the arithmetic operation they mimic, combining the fields modeled at each of its magnetometers.
Recent MEG systems include sophisticated online noise-attenuation techniques such as: higher-order gradient corrections and signal space projections. They contribute significantly to the basic model of data formation and therefore need to be taken into account (Nolte & Curio, 1999).