The Challenge
Most video games have a static difficulty level. We wanted to create a "closed loop" system where the game knows how hard your brain is working and adjusts itself accordingly.
The goal was to keep the player in a state of Flow. If they focused too hard (high cognitive load), the game would slow down to help them. If they relaxed or got bored, the game would speed up to challenge them.
My Approach
I was responsible for the backend logic that translated raw brain signals into game mechanics.
1. Signal Acquisition
We used the Unicorn Hybrid Black headset to capture live EEG data. The challenge was filtering out noise (like muscle movement) to get a clean signal of "mental work."
2. The Python Bridge
I wrote a Python script that acted as the middleware. It received UDP data packets from the headset, calculated the Alpha/Beta wave ratios in real-time to determine a "Workload Score."
3. Adaptive Game Loop
Based on the workload score, my script sent commands to the game engine.
High Load: "Bullet Time" (Ghosts slow down).
Low Load: "Chaos Mode" (Ghosts speed up).
The Outcome
We successfully built a working prototype where players could physically feel the game reacting to their focus levels.
It demonstrated that BCI (Brain-Computer Interfaces) can be used not just for medical purposes, but to create deeper, more responsive entertainment experiences.