PyNoetic让零编程经验者也能快速搭建EEG脑机接口，全流程可视化操作。
PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces
- 通过拖拽式流程图实现无代码设计，支持从数据采集到可视化全链路
- 集成特征提取、去噪、机器学习等工具，支持离线与实时双模式开发
- 适合神经科学、康复医学等跨领域研究者快速上手，降低技术门槛
基于脑电图（EEG）的脑机接口（BCI）在机器人、虚拟现实、医疗和康复等领域具有变革性潜力。然而，现有框架存在阶段灵活性不足、非编程背景研究者学习成本高、依赖商业软件导致成本上升、功能分散需多个外部工具等问题。为此，我们提出PyNoetic，一个模块化、全链条的Python BCI框架，涵盖刺激呈现、数据采集、通道选择、滤波、特征提取、伪迹去除、仿真与可视化等完整流程。其核心亮点是直观的端到端图形界面与独特的“拖放式”可配置流程图，支持无代码开发；同时为进阶用户保留低代码扩展能力，便于集成自定义算法。框架还内置多种分析工具，包括机器学习模型、脑连接度指标、模拟测试功能及新范式评估方法。PyNoetic兼具离线与实时开发能力，显著简化设计流程，使研究者能聚焦核心创新，加速科研进程。
原文摘要 · Abstract (English)
Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research. PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data acquisition to channel selection, filtering, feature extraction, artifact removal, and finally simulation and visualization. Notably, PyNoetic introduces an intuitive and end-to-end GUI coupled with a unique pick-and-place configurable flowchart for no-code BCI design, making it accessible to researchers with minimal programming experience. For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. PyNoetic also includes a rich array of analytical tools such as machine learning models, brain-connectivity indices, systematic testing functionalities via simulation, and evaluation methods of novel paradigms. PyNoetic's strengths lie in its versatility for both offline and real-time BCI development, which streamlines the design process, allowing researchers to focus on more intricate aspects of BCI development and thus accelerate their research endeavors. Project Website: https://neurodiag.github.io/PyNoetic
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