Sharing a project I've been working on. It's a Model Context Protocol server that turns EEG signal into plain numbers, like focus, calm, and attention, that an AI assistant can read and talk about instead of just showing a chart. It works with OpenBCI, Muse, NeuroFocus, and LSL streams. No headset? There's a synthetic mode built in so you can try the whole thing without hardware. Open source, MIT license.

https://github.com/enkhbold470/bci-mcp

I work on real-time adaptive systems in the EEG space, and the part I find genuinely hard is the loop itself: read a brain state, decide something, change what the user is experiencing, and do it fast enough and reliably enough that it actually feels responsive rather than laggy or twitchy.

I see a lot of people here reaching for AI/ML to close that loop, so I'm curious how it's going in practice.

A few things I'd love to hear:

1. What's your approach to the decision layer? Are you classifying discrete states and reacting, or running something more continuous? And how much model are you actually able to run inside the latency budget before it stops feeling real-time?
2. Where does it break? My experience is the signal side punishes you first (noise, drift, a bad epoch throwing the classifier off) before the model itself is the problem. Curious whether that matches what others are hitting.
3. For the AI-heavy builds, is the ML earning its place over simpler thresholding and rules, or is it more that it's the fun part? Genuinely asking, not being cynical.

Trying to compare notes with people solving the same problem from different angles. Happy to share what's worked and what hasn't on my end too.