[FieldTrip] common filter with more than two conditions

Arjen Stolk a.stolk8 at gmail.com
Tue Mar 8 01:28:11 CET 2016

Hey Lin,

Overall the more data (i.e. covariance matrices) the spatial filter is
constructed from the better it'll be able to describe the spatiotemporal
patterns present in the data (cf. ERPs, being smoother with more
averaging). The latter indeed may be condition-specific, and since I do not
know anything about your conditions, I'll refrain from having an opinion
there. :)


2016-03-07 16:16 GMT-08:00 Lin Wang <wanglinsisi at gmail.com>:

> Hi Arjen,
> Yes, it answers my question. Thank! :-)
> Then a further question is: is it better to include as many trials as
> possible to have a good estimation of the covariance matrix (provided that
> the signals are good for all trials)?
> For example, there are two experimental conditions, with 20 trials per
> condition. There are also 40 filler trials with a similar structure as the
> experimental conditions. In this case, can I combine all the conditions to
> build the common filter and then only compare the two experimental
> conditions later?
> The cognitive processes might be different between the experimental
> conditions and the fillers, so I'm not sure whether combining them has any
> influence on the spatial filter.
> Best,
> Lin
> On Tue, Mar 8, 2016 at 12:19 AM, Arjen Stolk <a.stolk8 at gmail.com> wrote:
>> Hey Lin,
>> Provided that there are no systematic confounds (e.g. head position)
>> across conditions, you could construct a common filter based on data from
>> all conditions. I would leave any statistical comparison to after
>> source-reconstruction.
>> Does that answer your question?
>> Arjen
>> 2016-03-07 1:51 GMT-08:00 Lin Wang <wanglinsisi at gmail.com>:
>>> Dear community,
>>> I'm trying to do lcmv beamformer source analysis with a common filter
>>> for more than two conditions. I have a 2A (A1, A2) * 2B (B1,B2) design, and
>>> I am interested in both the main effect of A (A1 vs. A2) as well as the
>>> simple effects (A1B1 vs. A2B1 and A1B2 vs. A2B2).
>>> My question is how to build the common filter. I could combine all the
>>> four conditions to obtain a common filter for the contrast of A1 vs. A2.
>>> Then can I also use this common filter to compare A1B1 vs. A2B1? Or do I
>>> have to build a different common filter (to combine the A1B1 and A2B1
>>> conditions) for the contrast of A1B1 vs. A2B1?
>>> Thanks for your help in advance!
>>> Best,
>>> Lin
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