What is ruv-neural?

rUv Neural is an open-source framework, written in Rust, for analyzing how the brain organizes activity in real time. It connects to EEG sensors (the electrode caps used in neuroscience labs) or to experimental quantum sensors, reads the incoming electrical signals, and builds a live map of which brain regions are talking to each other. That map is then analyzed with graph theory to detect shifts in cognitive state, such as when attention spikes, when confusion sets in, or when someone enters a focused flow state. The project is careful to say it is not mind-reading: it tracks the structure of brain activity, not specific thoughts or memories. Under the hood, the pipeline works in stages. Raw sensor data passes through digital signal processing to clean and filter it. The cleaned signals are turned into a connectivity graph that shows how strongly different brain regions are linked at any given moment. A minimum-cut algorithm then identifies the key boundaries in that graph, which correspond to meaningful transitions in cognitive state. All of this runs fast enough to be useful in real-time applications, and the code can also run on low-power microcontrollers (ESP32) or compile to WebAssembly for browser use. For developers who do not have EEG hardware, the project includes a built-in deterministic simulator that generates realistic neural signals. The quantum sensor front-end (NV-diamond and optically pumped magnetometers) is described as research-frontier territory: the hardware costs tens of thousands of dollars and is not something a typical developer would buy. EEG and the simulator are the practical starting points for most contributors. Potential uses described in the README include brain-computer interfaces that respond to intent, real-time cognitive load monitoring (compared to a fitness tracker for the brain), tools for stroke recovery, educational software that detects whether a student is following along, and next-generation gaming or VR systems. These are framed as research directions, not finished products, and the README notes that clinical uses require ethics board approval and regulatory clearance. The project ships as a set of Rust crates, passes 338 tests, and is licensed under MIT and Apache 2.0. It is an early-stage research framework rather than a commercial product, but the README is detailed about both what works today and what is still aspirational.