Lo-Fi Train Car is a real-time neurofeedback experience developed for Lucerum.ai. Unlike a traditional game, it has no controller, timers, enemies, or losing states — it is driven entirely by live brain-activity data streamed in from an EEG device over WebSocket and compared against a clinical threshold.
Players simply watch a relaxing train ride through a parallax lo-fi world. Staying above the target threshold smoothly accelerates the train, brightens the scene, and unlocks reward moments; dropping below it gently pauses progress without any punishing feedback — keeping the experience stress-free and clinically appropriate for sustained-focus and mindfulness training.
Key systems built for the project:
- Live WebSocket integration: Listens for "Trend" and "Absolute" score events from the neurofeedback data source and drives gameplay state in real time.
- Dynamic weather & scene engine: Seven distinct weather states (Sunny, Foggy, Snowy, Stormy, Starry, Windy, Raining) with animated parallax backgrounds, particle effects, and lighting transitions.
- Interior & audio layers: An animated train interior (including an ambient cat companion) plus adaptive background music and sound effects that fade and shift with session state.
- Configurable sessions: A settings overlay and config system let clinicians and users adjust scene/weather durations and audio preferences per session.
- Cross-platform packaging: Built once with Pixi.js and Vite, then shipped to Desktop (Electron) and Mobile (Capacitor for Android & iOS) alongside the web build.
The project demonstrates Chilipi's ability to build emotionally-tuned, clinically-driven interactive experiences — translating raw biometric data into a calm, rewarding visual journey rather than a conventional game loop.