The authors validated the full system end-to-end on in-house and public BCI datasets, training and updating decoders online in real time; each recording, decoder and training run is tracked in a database. This study is a preprint and has not been peer reviewed.
Preprint: Dendrite, a Real-Time Python Application for Online BCI Research and Development
Summary
A preprint introduces Dendrite, an open-source (GPL-3.0) Python application that bundles multimodal physiological signal acquisition, decoder training and real-time inference into a single modifiable application. It records multiple signal streams concurrently at their native sample rates, fits decoders either from a trained model or live during the pipeline, and tracks every recording, decoder and training run in a database so deployed decoders can be traced to their configuration and training source. The system was validated end-to-end on in-house and public BCI datasets, training and updating decoders in real time; the study has not been peer reviewed.
Why it matters
By making acquisition, decoder training and inference reproducible and traceable in one open-source tool, Dendrite addresses a reproducibility gap in online BCI research, where ad-hoc pipelines make results hard to audit or re-run.
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Sources · 1
arxiv.org 2026-07-16