Singh Gursimran, Chharia Aviral, Upadhyay Rahul, Kumar Vinay, Longo Luca
Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
PLoS One. 2025 Aug 6;20(8):e0327791. doi: 10.1371/journal.pone.0327791. eCollection 2025.
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(EEG)的脑机接口(BCI)已成为一项变革性技术,其应用涵盖机器人技术、虚拟现实、医学和康复领域。然而,现有的BCI框架面临若干限制,包括缺乏实验研究所需的逐阶段灵活性、对于没有编程专业知识的研究人员来说学习曲线陡峭、由于依赖专有软件而导致成本高昂,以及缺乏全面功能导致使用多个外部工具影响研究结果。为应对这些挑战,我们推出了PyNoetic,这是一个模块化的BCI框架,旨在满足BCI研究的多样化需求。PyNoetic是Python中极少数涵盖整个BCI设计流程的框架之一,从刺激呈现和数据采集到通道选择、滤波、特征提取、伪迹去除,最后到模拟和可视化。值得注意的是,PyNoetic引入了直观的端到端图形用户界面(GUI)以及独特的拖放式可配置流程图,用于无代码BCI设计,使编程经验最少的研究人员也能使用。对于高级用户,它便于以最少的编码无缝集成自定义功能和新颖算法,确保在每个设计阶段的适应性。PyNoetic还包括一系列丰富的分析工具,如机器学习模型、脑连接指数、通过模拟进行的系统测试功能以及新颖范式的评估方法。PyNoetic的优势在于其在离线和实时BCI开发方面的通用性,这简化了设计过程,使研究人员能够专注于BCI开发中更复杂的方面,从而加速他们的研究工作。