Liisa Helle (II)

Liisa Helle (II)

Hardware shielding: Internal Active Shielding (IAS) can be used to compensate external magnetic fields, in order to make sure all the channels stay in their dynamic range and measure magnetic fields throughout the recordings (especially magnetometers, which are sensitive to these external fields).

Some brain-originated fields may also be accidentally suppresed by this procedure, therefore it is essential to suppress the compensating fields with Signal Space Separation or Temporal Signal Space Separation when the data have been measured with internal active shielded on.

In case you are not sure about it, Elekta software will warn you if your data have been measured with Internal Active Shielding on.

The demo video offers an example to see the difference between data recorded with and without internal active shielded on, and how the procedure works.

Lecture 7: Internal Active Shielding

Lecture 8: Internal Active Shielding Demo

Here we move from the hardware shielding, to software shielding: signal space separation method. SSS method is based on the pphysical properties of magnetic fields (maxwell equations) and the fact that there are no current sources in MEG spaces where the SQUIDS are located.

Therefore, magnetic fields can be express as a gradient of harmonic scalar potential which satisfies the Laplace equation, which series form solution (spherical coordinates) has nice propierties for us. Besides, the masured magnetic field (presented as a signal vector field) also has a series form solution has with 2 components corresponding to internal and external parts of the system.

SSS is purely a spatial operation, no dependant on time.

The demo video offeres an example of basic SSS processing for continuous data with default settings.

Lecture 9: Signal Space Separation

Lecture 10: Signal Space Separation Demo

Temporal Signal Space Separation (SSS).

Sometimes SSS is not enough to suppress the artefacts and we need temporal extension of SSS. This occurs when there are closeby artefacts arising from intermediate spaces too close to the sensors. Then, pure SSS cannot suppress the sources because these artefacts leak into both internal and external basis, and are visible in signals not explained by the used model (residual signals).

There is time involved in tSSS, it cannot be performed to a single timepoint, but requieres a certain time-window. The number of projected components is data-dependant and can change from window to window.

When there are no closeby artefacts in the data, always reduce tSSS to SSS.

The demo video offers an example of tSSS for data with closeby artefacts: a subject with dental braces. When the subject breaths normally, the metallic particles move and cause artefacts. We will show you how to get rid of this artefact so you can do a normal data analysis afterwards

Lecture 11: Temporal Signal Space Separation

Lecture 12: Temporal SSS Demo

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Sometimes SSS is not enoguh, i.e. where the artefacts inducers are too close.

Then, a temporal extension of SSS is needed. It takes the correlation between 2 parts, and if exceds a threshold, it would be regarded as ean external source. There is time involved in the tSSS process, so it cannot be performed to a single time point, it requires a time window. It looks at time windows independently.