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Dear MNE team,
I needed to implement a csd estimation that return the single trials data.
For creating the common filter I used the pooled data (avg csd) but then I
apply the filter to single condition (with single trials). Keeping single
trials allow me to have a first level statistic condition vs baseline.
To do that:
- I don 't allow the function to average the csd at the end and I keep the
trials as second dimension ( for the function this work treating them as
different frequencies bin)
-I apply the filter at each trials, now the function instead of applying
the filter at each frequency it does it at each trials
I get a source power n vertices x n trials which is what I need, people
more experienced with the beamforming do they see any issue with this
approach ? In few words I used the same function csd_morlet and
apply_dics_csd but instead of the second dimension frequency I put trials.
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