> I like max-power, it may be long, but I really like that it's explicit.

ok good with me.

> And I just started thinking that 'pca' would in turn remind me more of something like Woolrich 2011 (MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization), who by the way use exactly the maximum power term when discussing orientation:

this Bayesian PCA is a bit different as it's to estimate data and noise covs.
btw if you want to tackle this you have everything in scikit-learn including
factor analysis… I did this but found no time to integrate for MEG… :( :(

> (...) defined as the projection of H that beamforms the maximum power, as in Sekihara et al.
> (2004)
>
> So I guess max-power is really good.
>
> Btw. I thought maybe making the pick_ori name even more explicit by changing it to source_ori would be nice? It then doesn't match the pick_normal, but in turn matches source_ori in the forward operator. And if you're thinking of changing pick_normal in MNE, than maybe it would make sense to change it both there and in LCMV to source_ori ? And then pick_ori = None would nicely and explicitly turn into source_ori = 'free' ?

what I like with pick is the notion that you discard some data.