Dendrite：用于在线脑机接口研究与开发的实时Python应用程序

中文摘要 AI 辅助

在线脑机接口研究需要能采集多模态生理数据、训练和更新解码器、进行实时推理并在可重现工作流程中保留完整实验出处的软件。我们展示了Dendrite，一个用Python编写的实时脑机接口应用程序，它将信号采集、解码器训练和实时推理整合在一个可修改的、随时可运行的应用程序中。Dendrite能同时记录多个信号流，以各自的原生速率运行，并对其并发执行多种处理模式。解码器可从先前训练的模型开始，或在管道运行时在会话中进行拟合，相同的记录可用于同一应用程序中的离线训练。每个记录、解码器和训练运行都在数据库中被跟踪，每个解码器记录其训练的配置和源记录。实验范式保持外部化，是通过网络连接Dendrite的任何语言编写的独立程序。我们在内部和公共BCI数据集上对整个系统进行了端到端验证，在管道实时运行时在线训练和更新解码器。Dendrite在该https URL下以GPL - 3.0许可开源。结果是一个用于开发和评估在线BCI范式的可重现、开源的生物医学计算系统。

英文摘要

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.

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15 pages, 5 figures