(I hope it’s okay if I moved this post outside of the private category as I think it’s of general interest – and I’m trying to bootstrap the forum )
The question of PC features for waveforms on multi electrode arrays is tricky in my mind. I haven’t studied the different sorters in detail w.r.t. this question… and I’ve always had the same question myself. People talk about PC features used in clustering as though they can all be put in the same space. But really there is more than one way to do it.
First, you could just compute per channel features. For example, 3 features per channel, and then you would have say 21 features if the waveform spanned 7 channels.
The way mountainsort (and others?) does it is to compute the features specific to a channel neighborhood by first flattening all the channels / timepoints into one big vector. But in this strategy you cannot compare the features across different neighborhoods… it just doesn’t work. And then there’s a question of what neighborhood is relevant for a particular cluster – and per spike neighborhood is even more difficult. So in the neighborhood strategy, there really is no such thing as PC features per event that is universal (across all events in the recording).
MountainSort has a multi-phase approach where a separate detection, feature extraction, and clustering is performed in each channel’s neighborhood. So if there are 64 channels, then 64 separate detect/feature/cluster procedures are performed (potentially in parallel). Then there is a way of removing duplicates (not merging!) and then a second phase of clustering. Because of the complexity I always need to pause when somebody asks “can you export the features used for clustering”. I simply don’t know how to answer it without describing the entire algorithm (a weakness of mine).
In summary my answer is “I don’t know”. And maybe somebody could help illuminate how the other sorters do it and whether it makes any sense to export PC features for large MEAs.