Jukka,
Thanks for your answer. The behavior I'm observing makes sense now.
I'll mark the channels as bad and count on SSS's ability to
reconstruct them. I'll also play with MaxST and see if I get better
results.
Best,
Gus
On Mar 2, 2009, at 2:36 AM, Jukka Nenonen wrote:
> 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
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