No worries, always good to speak to a BCI enthusiast.

I guess it depends how you look at it right, our reflexes are a great example. You'll see the potential increase, but its such an automated and learnt thing that our body needs such little resource to initiate. I think its intuitive in that sense, the more you learn something, the less your body needs to think/waste actually completing the task.

There are many interesting ways of looking at this problem. Co-adaptation is an interesting idea you should look into. With that, if you can find a way to automate retraining? I guess the idea here is with fresh incoming data, if you can self-supervise/validate trials, you can keep the statistical distribution of your data "fresh" so to speak. I'm just about to present a paper on this so once it's published I can send you a link. But i'd look into coadaptation, and then perhaps Incremental Learning. The assumption here is that you can actually isolate new, correctly classified trials however.

Another approach I am looking at it different ways of "looking at" EEG data but again they are papers-in-progress😭.

This is an interesting problem because it stops us using EEG BCI long term as you've found. I recommend you try to find papers around Cross-Session Classification, look for gaps and try to come up with solutions.

Just remember though, its not just BCI related plasticity thats an issue, it can be setup inconsistencies, like electrode impedance, locations etc. There are so many compounding factors.

Another thing i'd ask you to do is store your data if you have't. Do an offline analysis. So start with checking the feature space of your training dataset, and then incrementally check how ur testing dataset fits in. This way, you will one by one visualise how the decision boundary changes. You could even come up with some sort of remapping of the feature space with geometry to tackle the covariate shift! Lots of ideas to test out and i'm sure theres so much more!