Dendrite: A Real-Time Python Application for Online Brain-Computer Interface Research and Development

Abstract

Online brain-computer interface research requires software that can acquire multimodal physiological data, train and update decoders, run live inference, and preserve the full experimental provenance in a reproducible workflow. We present Dendrite, a real-time brain-computer interface application in Python that brings signal acquisition, decoder training, and live inference together in a single, ready-to-run application that stays modifiable. Dendrite records several signal streams at once, each at its native rate, and executes multiple processing modes concurrently against them. A decoder can start from a previously trained model or be fit mid-session while the pipeline keeps running, and the same recordings feed offline training in the same application. Each recording, decoder, and training run is tracked in a database, and every decoder records the configuration and the source recordings it was trained from, so a deployed decoder traces back to what produced it. The experimental paradigm stays external, an independent program in any language that reaches Dendrite over the network, rather than a module built inside the runtime. We validate the full system end-to-end on in-house and public BCI datasets, training and updating decoders online while the pipeline runs in real time. Dendrite is open-source under the GPL-3.0 license at https://github.com/dendrite-bci/dendrite. The result is a reproducible, open-source biomedical-computing system for developing and evaluating online BCI paradigms.