confusion about the results of combine evoke

I would like to know which is the best way to calculate the difference waves in the oddball paradigm?

The original oddball difference wave seems to be just a direct subtraction of the EEG values of the deviant and standard stimuli, and the code implementation of mne seems to be different from this?

I have some confusion about the principle and results of combine evoke.

If I get the standard stimulus minus the abnormal stimulus to produce a differential stimulus result (oddball paradigm).
The function provided by mne seems to be the combine evoke function.

But shouldn’t the total number of evokes be the same?
For example, if I have 100 standard stimuli and 100 abnormal stimuli, then

odd = mne.combine.evoked([std,dev],weights=[-1,1])

After that, shouldn’t there be 100 different stimuli?

But it seems that both mne’s own sample code and the one I came up with halve the number of stimuli.

And I’m curious what the difference is between these two codes? Why does the generated evoke have a different nave?
(dev_all evoke number =130 std_all evoke number = 130)
odd_all = mne.combine_d([dev_all, std_all], weights=[1, -1])

odd_all = mne.combine_d([dev_all, -std_all], weights=‘equal’)

Hi, @elvisjade
the .nave attribute of an Evoked object informs about the number of trials that were averaged to obtain the ERP. Since the difference wave is the difference between two averages (two ERPs), the .nave may no longer make much sense. But it depends on the weights that you use during combining - make sure that you read the Warning note from the documentation on mne.combine_evoked:
https://mne.tools/stable/generated/mne.combine_evoked.html

I think it’s also worth checking out what “nave” actually means and how we compute it. I don’t think this is properly documented outside of our source code, so here’s a link to the relevant bits:

Good point @richard. So, to extend my previous answer - as stated in the code comment the .nave parameter is used as an estimate of variance (1 / nave) during inverse operations (projecting data to source space). The variance accumulates when you perform difference operations (subtracting two noisy signals gives you even noiser difference) so the .nave changes to reflect that - and as a result is lower than your original .nave.

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Thank you very, very much for your reply.

I have understood the essence of nave, which seems to be a manifestation of variance.

I would also like to ask if that means
odd_all = mne.combine_d([dev_all, std_all], weights=[1, -1]) can correctly derive the value of the difference stimulus - standard stimulus?
I can now understand the reason for the variation, but I would like to ask if the values are correctly calculated?

Yes, this will calculate the difference and not apply any special weights when doing so. For a contrast like “deviant vs standard”, this is typically exactly what you want.

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Thank you very, very much!
I understand already.
Thank you for answering my questions.

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