List of Abbreviations

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.基于脑电图（Electroencephalography, EEG）的脑机接口（Brain-Computer Interfaces, BCIs）已成为一项变革性技术，应用场景覆盖机器人学、虚拟现实、医学与康复领域。然而，现有脑机接口框架仍存在诸多局限：缺乏实验研究所需的分阶段灵活性；对于无编程经验的研究人员而言学习门槛极高；因依赖专有软件导致使用成本攀升；且功能不够全面，需借助多款外部工具开展研究，进而对实验结果造成不利影响。为解决上述问题，本研究提出PyNoetic——一款专为满足脑机接口研究多样化需求而设计的模块化框架。PyNoetic是目前为数不多的支持完整脑机接口设计全流程的Python框架之一，覆盖从刺激呈现、数据采集，到通道选择、滤波处理、特征提取、伪迹去除，最终到仿真与可视化的全部环节。尤为值得一提的是，PyNoetic搭载了直观的端到端图形用户界面（Graphical User Interface, GUI），并配套独特的拖拽式可配置流程图工具以支持无代码脑机接口设计，即便编程经验匮乏的研究人员也可轻松上手。对于资深用户而言，该框架仅需少量代码即可实现自定义功能与新型算法的无缝集成，确保各设计阶段均具备良好的可扩展性。PyNoetic还内置了丰富的分析工具，涵盖机器学习模型、脑连接指数、基于仿真的系统化测试功能，以及新型实验范式的评估方法。PyNoetic的核心优势在于其同时支持离线与实时脑机接口开发的通用性，能够简化设计流程，使研究人员得以专注于脑机接口开发中更复杂的核心环节，进而加速研究进程。