Dear Gustavo,
1) The discontinuity arises due to a saturating channel. At the jump
the signal exceeds the
dynamic range, and then slowly recovers back.
Often the saturation arises due to bad tuning of a channel. After
heating you should try
improve the tuning. If it still persists there can be a hardware
problem with that channel.
2) During the jump and recovery, the signal at that channel does not
obey Maxwell equations
and thus the spatial SSS spreads the artifact over several channels. The
easiest solution
is to mark the channel as a static bad one. SSS can reconstruct the bad
channel from
other channels without loosing any information.
3) Splitting the file or using the '-skip' option is more clumsy because
you first need to find
the times of jumps and then discard these periods.
4) The best way is to avoid discontinuities, either by setting static
bad channels manually
or by using the temporal extension (MaxST in the user guide).
5) MaxST may sometimes produce discontinuities at buffer boundaries (def
buffer is 4 secs).
If the boundary is at the period when a staurated channel is recovering,
MaxST does not
guarantee continuity and in fact sees differing interference
contributions in the buffers on
the 'left' and 'right'.
Thus, I recommend to browse the data to detect if there are jumping
channels, and mark
them as static bad ones (remembering the limitiations on maxfilter 2.0
autobad function).
You should use your own judgement to decide if MaxST is needed or not.
Usually,
MaxST improves the result and does no harm even if there are no remaining
sensor-space components.
Best regards, Jukka Nenonen and Samu Taulu
Gustavo Sudre wrote:
> Hello,
>
> For many repetitions in my experiments I have observed a discontinuity
> in the raw MEG data. This happens in some of my individual trials, and
> it is not always across several channels simultaneously. The attached
> picture shows an example of this type of noise, for a particular trial
> and channel. When I notice such pattern during recordings, reheating the
> sensor usually works. However, I don't catch them all the time, so it's
> often the case that I see this pattern in recorded data. These are my
> questions:
>
> 1) What causes this noise? Can the channel be "trusted" even if it
> shows the pattern a few times?
>
> 2) If I run my data through SSS, this pattern seems to appear in many
> more channels. That means that I need to discard many more repetitions
> after running SSS. Is this something intrinsic to the SSS algorithm
> (i.e. to multiply this noise)? What is the reason for it to appear
> across more channels after SSS?
>
> 3) If I clean up my raw data prior to SSS (e.g. discard the repetitions
> with such pattern), and create a new FIF raw file from this new data, my
> data won't be continuous in time anymore. I don't think SSS will have a
> problem with it (unless I use the temporal extension). Is that correct?
>
> 4) Would you suggest a more accurate way to deal with these
> discontinuities (eg. wavelets?), instead of discarding (sometimes
> precious) trials?
>
> 5) Assuming SSS has no problems with this new "clean" raw file, can the
> SSS algorithm create such discontinuities by itself? If so, why?
>
> Thanks,
>
> Gus
--
================================
Dr. Jukka Nenonen
Manager, Method development
Elekta Neuromag Oy
Street address: Siltasaarenkatu 18-20A, Helsinki, Finland
Mailing address: P.O. Box 68, FIN-00511 HELSINKI, Finland
Tel: +358 9 756 240 85 (office), +358 400 249 557 (mobile),
+358 9 756 240 11 (fax), +358 9 756 2400 (operator)
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