It's interesting that the model generalizes to unseen participants. I was under the impression that everyone's brain patterns were different enough that the model would need to be retrained for new users.

Though, I suppose if the model had LLM-like context where it kept track of brain data and speech/typing from earlier in the conversation then it could perform in-context learning to adapt to the user.

Basically correct intuition: the model does much better when we give it, e.g., 30 secs of neural data in the leadup instead of e.g. 5 secs. My sense is also that it's learning in context, so people's neural patterns are quite different but there's a higher-level generator that lets the model learn in context (or probably multiple higher-level patterns, each of which the model can learn from in context).

We only got any generalization to new users after we had >500 individuals in the dataset, fwiw. There's some interesting MRI studies also finding a similar thing that when you have enough individuals in the dataset, you start seeing generalization.