Hi,
I am trying to do cluster-based permutation tests on my MEG data (Neuromag, 306 channels). As I don’t have a reason to prefer one sensor type of another (mags vs grads) and there is no adjacency for all 306 sensors, I run all analyses separately for both sensor types, which is a little annoying. So when I stumbled upon the method epochs.as_type(), it seemed like an easy way to combine the sensor types. Also in the accompanying example, it says that using this function is a way to run statistics on both sensor types at the same time.
This sounds to good to be true, so I wondered whether this is really as easy. Can I just map my gradiometers to (virtual) magnetometers, and then run a cluster-based permutation test on all 306 channels, using the adjacency matrix of the magnetometers, or is there more to it?
Thanks,
Eduard