MNE 1.6.0 on Manjaro Linux i3

Hello,

I am an EEG noob trying to perform source estimation and connectivity analyses on continuous EEG.

In this case, I tried using the `mne.minimum_norm.source_band_induced_power()`

on my data - which is basically a 10 minute (600 second) 128 channel EEG split into 5 second epochs. I’ve mainly done this just so that my computer can handle it RAM-wise.

The stc that comes out is number of vertices in the source space x 5 seconds long. I just want to fully be sure of this function’s behavior:

- Is the stc that comes out basically an average of all the induced power vs time plots for every epoch (i.e. 120 equally spaced epochs)?
- What is
`n_cycles`

exactly? - What is the best way of producing an stc that is 10 minutes long? I can’t use the raw because it would require terabytes of RAM, which I don’t have (I have 128 GB, and running the above with significant downsampling for just a single frequency band like gamma consumes at least half my RAM for the duration of the run). I’ve thought of running a for loop for each epoch[i], turning the stc[i] into a numpy array then concatenating, but if there’s a better option I’d appreciate it.
- What is the function actually doing? Is it band-pass filtering the epochs for each frequency band then getting a source estimate? Or something more sophisticated?

Thank you