An AI/ML-ready closed loop BCI simulation framework

总结 通信脑机接口系统（cBCI），如在主裁决R 01 DC 009834中所述，必须 为有严重语言和身体障碍的最终用户提供个性化服务， 可能在家中使用。如果一个人想通过脑电波打字来表达自己 （特别是，RSVP键盘和P300事件相关的潜在意图选择），系统必须可靠， 高效准确大多数cBCI系统目前缺乏适应用户不断变化的需求的能力， 随着时间的推移，技能，导致准确性和有效性降低。这种无法适应最终用户内部 状态、偏好和上下文是增强系统基础设施和自适应能力的驱动动机。 建模以促进AI/ML准备就绪。拟议的增强功能和模拟框架将使 系统更加强大，并允许研究人员共享由FAIR标准驱动的标记EEG数据集。电流 cBCI不具备测试打字模型的能力，也没有适当的护栏， 对于AI的实现是必要的。本补充建议通过添加元参数来改进基础设施 使用我们现有的数据集，并为其他cBCI构建灵活的任务模拟器， 研发团队。将人工智能和自适应建模集成到cBCI中将有助于克服当前的 通过包含更细粒度的控制，为在线优化、模拟和 用户偏好。提出了两个具体目标：SA 1。构建闭环BCI控制的任务模拟 该框架有两个次级目标：SA 1a。为快速模型原型构建任务模拟器; SA 1b。优化 用于最终用户意图推断的闭环决策过程。作为产品，我们将提供结果数据， 通过公共版本控制平台和数据仓库模拟器代码。SA 2.发展基础设施， 支持在多模式环境中集成自适应模型和AI。将创建一个演示实体 其中融合、决策和加权参数可用于跨证据和处理管道进行更新。 最终标记的数据将存储在一个单独的文件（.fif）中，并带有工件注释的文本导出以供使用 在MNE和BciPy之外。半自动工件管道将在我们的GitHub仓库中公开提供： https://github.com/CAMBI-tech. BciPy开发团队（在Memmott先生的指导下）和 信号处理和机器学习团队（在Erdogmus和Imbiriba博士的指导下），有指导 自2009年首次资助母基金以来，来自临床团队（Dr. Fried-Oken）的20位专家一直在合作。 他们的成功产出将在本项目中得到进一步完善。创建一个闭环任务模拟器， 自适应建模的元参数将显著加快模型比较，降低模型成本 探索和原型设计，增强最终用户波动技能和需求的定制，并增加 更广泛的BCI社区的数据访问。

Summary Communication brain-computer interface systems (cBCIs), as stated in the parent award R01DC009834, must be personalized for end-users with severe speech and physical impairments to make them as effective as possible for potential in-home use. If individuals are going to express themselves by typing with brainwaves (specifically, the RSVP Keyboard and P300 event-related potential for intent selection), systems must be reliable, efficient, and accurate. Most cBCI systems currently lack the ability to adapt to the user's changing needs and skills over time, leading to reduced accuracy and effectiveness. This incapacity to adapt to the end-user's internal states, preferences, and context is a driving motivation for enhancing system infrastructure and adaptive modeling to facilitate AI/ML readiness. The proposed enhancements and simulation framework will make the system more robust, and permit researchers to share labeled EEG datasets driven by FAIR standards. Current cBCIs do not have the capabilities of testing models for typing, nor do they have guardrails in place that are necessary for AI implementation. This supplement proposes improving infrastructure by adding meta-parameters to the BciPy open-source platform with our existing datasets and building a flexible task simulator for other cBCI research and development teams. Integrating AI and adaptive modeling into cBCIs will help overcome current limitations by including finer-grained controls that open up opportunities for online optimization, simulation, and user preference. Two specific aims are proposed: SA1. Build a closed-loop BCI-controlled task simulation framework, with two sub-aims: SA1a. Construct a task simulator for rapid model prototyping; SA1b. Optimize the closed-loop decision process for end-user intent inference. As products, we will provide the resulting data and simulator codes through public version control platforms and data repositories. SA2. Develop infrastructure to support integrating adaptive models and AI in a multimodal environment. An Orchestration entity will be created with fusion, decision, and weighting parameters available for updating across evidence and processing pipelines. The final labeled data will be stored in a separate file (.fif) with a text export of the artifact annotations for use outside MNE and BciPy. The semi-automatic artifact pipeline will be publicly available at our GitHub repo: https://github.com/CAMBI-tech. The BciPy Development Team (under the direction of Mr. Memmott) and the Signal Processing & Machine Learning Team (under the direction of Drs. Erdogmus and Imbiriba), with guidance from the Clinical Team (Dr. Fried-Oken) have been collaborating since the parent award was first funded in 2009. Their successful outputs will be further refined in this project. Creating a closed-loop task simulator and exposing meta-parameters for adaptive modeling will significantly speed up model comparisons, reduce costs of model exploration and prototyping, enhance customization for an end-user's fluctuating skills and needs, and increase data accessibility for broader BCI communities.