BCIJelly：用于脑机接口研究的集成生态系统
BCIJelly: An integrated ecosystem for brain-computer interface research
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中文总结 AI 辅助
BCIJelly是集成多类BCI数据集、解码器及工具的Python生态系统，可通过AAS与LLM支持多任务跨物种解码，经多范式多物种验证，为BCI研究提供统一可扩展的开发部署基础设施。
中文摘要 AI 辅助
脑机接口（BCI）研究依赖多阶段计算流程，但进展仍受限于碎片化的数据格式、异构解码器实现以及特定于硬件的部署工具链，研究人员缺乏集成化的工作流程。在此，我们通过BCIJelly填补这一空白，它是一个统一的计算生态系统，在单个Python框架内集成了18个精选BCI数据集、15个基准解码器和包含80个可复用模块的算法库、自动架构搜索（AAS）流程，以及通过toChip管道实现的硬件感知部署。AAS无需手动设计架构即可构建特定任务的解码器，它进一步扩展为受大语言模型（LLM）引导的闭环模式，LLM利用任务规格、模块描述和搜索历史来支持多任务和跨物种解码。toChip管道将训练好的解码器编译为可在神经形态芯片上执行的形式，为BCI系统实现高能效部署。配套的可视化软件提供了完整工作流程的图形界面，无需编程即可使用BCIJelly。我们在五种BCI范式（运动、视觉、言语、情感和听觉）上验证了BCIJelly，涵盖人类、猕猴和小鼠的记录，以及单任务、多任务和跨物种解码设置。BCIJelly建立了统一且可扩展的基础设施，为BCI研究架起了解码器开发与硬件感知部署之间的桥梁。
英文摘要
Brain-computer interface (BCI) research relies on multistage computational pipelines, yet progress remains constrained by fragmented data formats, heterogeneous decoder implementations and hardware-specific deployment toolchains, and researchers lack an integrated workflow. Here, we fill this gap with BCIJelly, a unified computational ecosystem that integrates 18 curated BCI datasets, 15 benchmark decoders and an algorithmic library of 80 reusable modules, an automated architecture search (AAS) procedure, and hardware-aware deployment through the toChip pipeline within a single Python framework. AAS constructs task-specific decoders without manual architecture design. It is further extended into a closed-loop mode guided by a large language model (LLM), which uses task specifications, module descriptions and search history to support multitask and cross-species decoding. The toChip pipeline compiles trained decoders for execution on neuromorphic chips, enabling energy-efficient deployment for BCI systems. An accompanying visualization software provides a graphical interface to the full workflow, making BCIJelly accessible without programming. We validate BCIJelly across five BCI paradigms (motor, visual, speech, emotion and auditory) with recordings from humans, macaques and mice, and single-task, multitask and cross-species decoding settings. BCIJelly establishes a unified and extensible infrastructure that bridges decoder development and hardware-aware deployment for BCI research.
发表机构
- Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences（中国科学院神经科学研究所脑科学与智能技术卓越创新中心）
- Lingang Laboratory（临港实验室）
- Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
- School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
- State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology（脑认知与类脑智能技术全国重点实验室）
机构由 AI 辅助整理，请以论文原文为准。
