Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit

总结 美国估计有400万成年人患有严重的语言和身体障碍（SSPI）。 神经发育或神经退行性疾病的患者不能依靠现有的辅助技术 (AT)交流的工具在一天内，或者随着疾病的进展，他们可能会从一个访问过渡到另一个访问。 由于疲劳、药物、身体状况改变或进行性运动功能障碍，将技术转移给另一个人。 目前还没有临床或AT解决方案能够适应这些患者的多种动态访问需求， 个人，使许多人服务不佳。这种竞争性的更新，称为BCI-FIT（脑计算机 接口功能实现工具包）增加了我们的创新多学科转化研究 在过去11年中进行了与非侵入性BCIs相关的科学进步， 这些临床人群。BCI-FIT依赖于主动推理和迁移学习来定制一个完整的 自适应意图估计分类器可以实时地对每个用户的多个模态信号进行自适应意图估计分类。BCI-FIT缩写 具有许多含义：我们的BCI适合每个用户的大脑信号;对环境，提供相关的个人信息。 语言;用户的内部状态，调整信号的基础上困倦，药物，身体和 认知能力;以及从BCI介绍到专家使用的用户学习模式。 提出了三个具体目标：（1）开发和评估优化系统和用户的方法 性能与多模态信号模型的在线，鲁棒的适应。(2)制定和评估方法， 通过主动查询进行有效的用户意图推断。(3)集成语言交互和字母/单词 补充作为实时BCI使用中的输入模态。四个单一案例的实验研究设计将 评估用户性能和技术性能，以便与35名参与者进行功能沟通 与SSPI在社区，和30名健康对照进行初步测试。相同的因变量将 在所有实验中测试：打字准确度（正确的字符选择除以总的字符选择）， 信息传输率（ITR）、打字速度（正确字符数/分钟）和用户体验（UX）调查问卷 关于舒适度、工作量和满意度的反应。我们的目标是建立个性化的建议 根据临床和机器专业知识的组合为每个用户提供。临床专业知识加上用户反馈 添加到主动传感器融合和强化学习的意图推理将产生优化的多模态 每个最终用户的BCI都可以根据短期和长期波动功能进行调整。我们的研究是在 由四个成功合作实施转化科学的子团队组成：电气/计算机 工程学;神经生理学和系统科学;自然语言处理;和临床康复。 该项目以坚实的机器学习方法为基础，采用参与式行动研究模型， 审计咨询委员会的参与。该项目将改进技术和BCI技术能力，展示BCI 实施范例和严重残疾人的临床指南。

SUMMARY Many of the estimated four million adults in the U.S. with severe speech and physical impairments (SSPI) resulting from neurodevelopmental or neurodegenerative diseases cannot rely on current assistive technologies (AT) for communication. During a single day, or as their disease progresses, they may transition from one access technology to another due to fatigue, medications, changing physical status, or progressive motor dysfunction. There are currently no clinical or AT solutions that adapt to the multiple, dynamic access needs of these individuals, leaving many people poorly served. This competitive renewal, called BCI-FIT (Brain Computer Interface-Functional Implementation Toolkit) adds to our innovative multidisciplinary translational research conducted over the past 11 years for the advancement of science related to non-invasive BCIs for communication for these clinical populations. BCI-FIT relies on active inference and transfer learning to customize a completely adaptive intent estimation classifier to each user's multiple modality signals in real-time. The BCI-FIT acronym has many implications: our BCI fits to each user's brain signals; to the environment, offering relevant personal language; to the user's internal states, adjusting signals based on drowsiness, medications, physical and cognitive abilities; and to users' learning patterns from BCI introduction to expert use. Three specific aims are proposed: (1) Develop and evaluate methods for optimizing system and user performance with on-line, robust adaptation of multi-modal signal models. (2) Develop and evaluate methods for efficient user intent inference through active querying. (3) Integrate language interaction and letter/word supplementation as input modalities in real-time BCI use. Four single case experimental research designs will evaluate both user performance and technology performance for functional communication with 35 participants with SSPI in the community, and 30 healthy controls for preliminary testing. The same dependent variables will be tested in all experiments: typing accuracy (correct character selections divided by total character selections), information transfer rate (ITR), typing speed (correct characters/minute), and user experience (UX) questionnaire responses about comfort, workload, and satisfaction. Our goal is to establish individualized recommendations for each user based on a combination of clinical and machine expertise. The clinical expertise plus user feedback added to active sensor fusion and reinforcement learning for intent inference will produce optimized multi-modal BCIs for each end-user that can adjust to short- and long-term fluctuating function. Our research is conducted by four sub-teams who have collaborated successfully to implement translational science: Electrical/computer engineering; Neurophysiology and systems science; Natural language processing; and Clinical rehabilitation. The project is grounded in solid machine learning approaches with models of participatory action research and AAC participation. This project will improve technologies and BCI technical capabilities, demonstrate BCI implementation paradigms and clinical guidelines for people with severe disabilities.