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Q: mcca inverse transform #33
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Hi @kingjr, just to be sure : the use-case is denoising a single subject data based on a dataset of multiple subjects performing the same task ? If so the current code in Lines 31 to 38 in 35d79f3
Using the taxonomy in de Cheveigné et al. (2018), the MCCA code produces both summary components (SC) and canonical correlates (CC):
So if I understand correctly what you want is essentially to take the (edited mistake) |
Thanks @nbara. If I follow Alain's interpretation of MCCA, it ends with a PCA, so i think the CC should be orthogonal, shouldn't they? But yes, I'm looking for the canonical -> sensor matrix. Shall I just invert |
Yes I think that should work. I'm going to perform some tests soon to verify it. Will report back here when I have some results. |
Hi @nbara ,
Thanks for this nice package.
IIUC, mcca generate a matrix that project sensors onto canonical components.
Is there a reverse transform easily available e.g. project many subjects on canonical components, average, and project back on one?
I think this should be doable given that canonical components are orthogonal and thus invertible?
Thanks!
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