Artifact cleaning with SSP
It is common to have portions of recordings contaminated by events coming from the subject (eye blinks, movements, heartbeats, teeth clenching, implanted stimulators...) or from the environment (stimulation equipment, elevators, cars, trains, building vibrations...). Some of them are well defined, reproducible, short and frequent, and can be removed efficiently using Signal Space Projections (SSP). The purpose of this tutorial is to introduce this technique to correct for the cardiac and ocular artifacts.
For this tutorial, we are going to use the protocol created in the previous tutorial ?Review continuous recordings and edit markers. If you have not followed this tutorial yet, please do it now.
Signal Space Projection
The Signal-Space Projection (SSP) is one approach to rejection of external disturbances. Here is a short description of the method by Matti Hämäläinen, from the MNE 2.7 reference manual, section 4.16.
Unlike many other noise-cancellation approaches, SSP does not require additional reference sensors to record the disturbance fields. Instead, SSP relies on the fact that the magnetic field distributions generated by the sources in the brain have spatial distributions sufficiently different from those generated by external noise sources. Furthermore, it is implicitly assumed that the linear space spanned by the significant external noise patterns has a low dimension.
Without loss of generality we can always decompose any n-channel measurement b(t) into its signal and noise components as:
b(t) = bs(t) + bn(t)
Further, if we know that bn(t) is well characterized by a few field patterns b1...bm, we can express the disturbance as
b(t) = Ucn(t) + e(t) ,
where the columns of U constitute an orthonormal basis for b1...bm, cn(t) is an m-component column vector, and the error term e(t) is small and does not exhibit any consistent spatial distributions over time, i.e., Ce = E{eeT} = I. Subsequently, we will call the column space of U the noise subspace. The basic idea of SSP is that we can actually find a small basis set b1...bm such that the conditions described above are satisfied. We can now construct the orthogonal complement operator
P⊥ = I - UUT'
and apply it to b(t) yielding since The basis set The angles between the noise subspace space spanned by If the first requirement is not satisfied, some noise will leak through because Since the signal-space projection modifies the signal vectors originating in the brain, it is necessary to apply the projection to the forward solution in the course of inverse computations. For more information on the SSP method, please consult the following publications:
The first step for this method is to identify a large number of examples of the artifact.
Select the protocol Right-click on the continuous recordings again >
b(t) ≈ P⊥bs(t) ,
b1...bm should be able to characterize the disturbance field patterns completely and Identifying the artifact
Manual marking
TutorialRaw created in the previous tutorial, and select the "Functional data" view (second button in the toolbar on top of the database explorer). Automatic detection