Downsampling source localised data along spatial dimensions

[Proxima]

Dear Francois,

I have the following data structure -

40 participants, 10 x 60sec epochs, MEG data Fs 250Hz
The data has been source localised (LCMV) using the 15000V cortical surface (generated using FreeSurfer). As you can imagine, the output has become huge! Furthermore, I also want to estimate source level coherence in 4 frequency bands. This will replicate these files 4 times (400x4), this isn't the most efficient use of the storage space.

So I was wondering if it is possible to downsample the source localised data in spatial dimension?
I know how to downsample data along the time dimension. Ideally, I would like the final size to be 8k vertices x 6000 samples in the time domain (Fs: 100Hz).

Please let me know.

Thanks!

[Francois]

@Proxima

So I was wondering if it is possible to downsample the source localised data in spatial dimension?

Two approaches:

• Using a lower resolution for the cortex surface: Right-click on the HIGH-resolution cortex surface in the subject anatomy > Less vertices. Set the new surface as the default cortex surface, delete all the forward and inverse models, and recompute them with the new surface.
• Work with ROIs: Extract the scouts time series using an anatomical atlas (volume or surface), and compute the coherence from these new signals:
  https://neuroimage.usc.edu/brainstorm/Tutorials/LabelFreeSurfer#Cortical_parcellations
  https://neuroimage.usc.edu/brainstorm/Tutorials/SegCAT12#Cortical_parcellations
  https://neuroimage.usc.edu/brainstorm/Tutorials/Scouts
  https://neuroimage.usc.edu/brainstorm/Tutorials/TutVolSource