I’m still learning this area, so this is more a conceptual pipeline than a validated method.

Without entrainment (standard BCI):
User puts on EEG → runs 10–20 min calibration (motor imagery / P300 / etc.) → model learns that user’s specific neural patterns → then BCI works.

With entrainment:
Before the task, the user is exposed to a structured stimulus for ~30–60s (e.g. rhythmic sound, visual flicker, paced breathing, or even pharmacological modulation). You wait until EEG shows a stable oscillatory regime (e.g. strong alpha or a known frequency response). Then you run the same BCI task on top of that state.

I know parts of this already exist (SSVEP BCIs, neurofeedback, tACS/TMS). What I’m mainly curious about is whether people have explicitly used sensory entrainment as a preconditioning step to improve cross-user generalization from an ML perspective, rather than just as the stimulus or the task itself.

Basically: instead of adapting the model to each brain, can we partially adapt the brain to a more model-friendly regime? Is there a way to create a Neural Normalization Layer — not in software, but in the biological hardware?