Case Study
NeuralEncoding - Desktop Neural Telemetry Dashboard
NeuralEncoding is an operator-style desktop monitoring prototype for EEG-like telemetry. It combines an Electron shell, React interface layers, and a Python FastAPI stream source to deliver a full-screen, high-density telemetry experience.
Role
Lead Front-end Developer
Platform
Desktop (Electron)
Frontend
React + Vite
Backend
Python + FastAPI
Problem
A browser-first interface felt too generic for continuous telemetry monitoring. The product needed to behave like a dedicated workstation surface while still handling dense real-time data, logs, and diagnostic indicators without visual clutter.
Demo
Local project demo video from the public directory.
Solution
- Built a dedicated desktop shell so the monitoring experience feels like an operator workstation, not a browser tab.
- Implemented full-screen first launch behavior with fast keyboard exit handling for focused review sessions.
- Presented dense telemetry with a clear visual hierarchy across waveform, signal quality, status, and event panels.
- Hid scrollbars while preserving mouse and trackpad scrolling to keep the UI clean without reducing usability.
- Addressed theme-specific shell edge artifacts to keep visual consistency across system light and dark modes.
Architecture
Desktop shell
Electron loads the compiled main process entry and hosts the monitoring surface in a desktop window.
Frontend monitor
React views render live feed and system diagnostics panels with a unified telemetry visual language.
Stream source
FastAPI and Uvicorn run as a separate backend process that provides telemetry stream data.
Tech Stack
Important Note
Current stream behavior appears is simulated for prototype and operator-testing scenarios.