Mathieu Scheltienne
Mathieu Scheltienne
I picked up this PR, and started with (1) clean-up, (2) input validation on the class, (3) I/O roundtrip to FIFF. No test for now, but that should be a...
But if we have `fitted_data`, we should be able to compute the `labels`? So in the end, we compute the GEV in all cases except if `fitted_data` is not provided?
Sounds like `labels` should not be auto-calculated.
Adding one more bullet point: - [ ] I/O roundtrip!
I'm not very knowledgeable on MEG microstates, but for MEG sensor space analysis between subjects and/or between recordings/sessions, you should transform all your recordings towards a common head position, or...
Again, not familiar that much with MEG microstates or the HCP dataset. You do have [mne-hcp](https://github.com/mne-tools/mne-hcp) to interact with this specific dataset. MAybe @Shrecki might be able to give you...
The digitization gives you the sensor geometry, which for MEG is fixed. Thus, the fix of addijg the sensor geomtry manually to your files from another recording with the same...
@vferat Any reason why we excluded Evoked? I guess you could bypass this by using a `ChData` object: https://pycrostates.readthedocs.io/en/latest/api/generated/pycrostates.io.ChData.html#pycrostates.io.ChData ``` evoked = Evoked(...) ch_data = ChData(evoked.get_data(), evoked.info) ```
No that was from a generic method, not expecting any reference (could actually be applied to any channel type..)
All good, it was already patch. Now 'gfp' is an alias for std for EEG sensors, and RMS for MEG sensors.