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

摘要 据估计，美国有400万成年人患有严重的语言和身体障碍(SSPI) 由神经发育或神经退行性疾病引起的疾病不能依赖现有的辅助技术 (At)用于交流。在一天中，或随着疾病的发展，他们可能会从一种途径过渡到 由于疲劳、服药、身体状态改变或进行性运动功能障碍，技术转移到另一种。 目前还没有临床或AT解决方案来适应这些设备的多种动态访问需求 个人，使许多人的服务质量很差。这种竞争性更新称为BCI-FIT(Brain Computer 界面功能实施工具包)为我们创新的多学科翻译研究增添了新的内容 在过去11年里，为促进与用于通信的非侵入性BCI相关的科学进步而进行的 对于这些临床人群。BCI-FIT依靠主动推理和迁移学习来完全定制一个 实时对每个用户的多个通道信号进行自适应意图估计分类器。BCI-Fit缩写 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 语言；根据用户的内部状态，根据昏昏欲睡、药物治疗、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 具有在线、稳健的多模式信号模型自适应性能。(2)开发和评估方法 通过主动查询进行高效的用户意图推理。(3)语言互动和字母/单词相结合 补充作为实时脑-机接口使用的输入模式。四个单案例实验研究设计将 评估用户绩效和技术绩效，以便与35名参与者进行功能性沟通 在社区进行SSPI调查，并与30名健康对照进行初步检测。相同的因变量将 在所有实验中进行测试：打字准确性(正确字符选择除以总字符选择)， 信息传输率(ITR)、打字速度(正确字符/分钟)和用户体验(UX)问卷 关于舒适度、工作量和满意度的回答。我们的目标是建立个性化的推荐 基于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 加入主动传感器融合和强化学习进行意图推理，将产生优化的多模式 针对每个终端用户的BCI，可以根据短期和长期波动函数进行调整。我们的研究是进行的 由成功合作实施翻译科学的四个子团队：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和 瑞声参与。该项目将提高技术和BCI技术能力，展示BCI 针对严重残疾人的实施范例和临床指南。

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.