Clinic Interactions of a Brain-Computer Interface for Communication

 描述（由申请人提供）：脑机接口（BCI）用于交流的承诺正在成为严重言语和身体障碍（SSPI）的个人的现实，他们无法依靠言语或写作来表达自己。虽然大多数研究工作都致力于技术开发，以解决稳定性，可靠性和/或分类问题，但临床和行为挑战变得越来越明显，因为SSPI患者及其家庭/护理团队在新手或长期试验期间评估系统。RSVP Keyboard（TM）BCI转化研究团队的目标是通过创新的工程设计解决功能性BCI使用过程中提出的临床挑战，从而增强这种新型辅助技术的潜力。提出了四个具体目标：（1）开发BCI通信应用套件（BCI-CAS），为SSPI患者提供一组语言模块，以满足他们的语言/读写能力;（2）开发改进的统计信号模型，用于个性化特征提取、伪影/干扰处理以及从生理信号中提取鲁棒、准确的意图证据;（3）开发改进的语言模型和刺激序列优化方法;（4）评估影响BCI-CAS学习和表现的认知变量。五个语言模块，提出了依赖于一个多模态的证据融合框架，基于模型的上下文感知的最佳意图推理：RSVP键盘（TM）生成拼写; RSVP短信; RSVP在上下文中打字; RSVP在上下文中的图标打字;和二进制是/否响应SSVEP。当前RSVP Keyboard（tm）和SSVEP系统的可用性数据驱动了所有提出的目标。用户选择语言模块，BCI系统基于用户适应、意图推断和个性化语言建模来优化每个人的性能。独特的模拟功能可实现系统参数的个性化。BCI定制工作的鲁棒性由具有SSPI和神经典型控制的成年人以迭代的方式持续评估。三个干预方案，解决注意力的认知结构（过程特定的注意力训练，正念冥想训练和新的刺激呈现）的效果将通过假设驱动的单一主题设计实施。30名年龄在21岁及以上的SSPI参与者将被纳入家庭干预。通过测量信息传输率（ITR），用户满意度和内在的用户因素，我们将确定学习策略，影响脑机接口的门槛收购和性能的成人神经退行性或神经发育条件。翻译团队包括（1）信号处理（Erdogmus）;（2）临床神经生理学（Oken）;（3）自然语言处理（Bedrick/Gorman）;和（4）辅助技术（Fried-Oken）。我们继续依赖于坚实的贝叶斯基础和理论框架：ICF残疾分类（WHO，2001），AAC参与模型（Beukelman & Mirenda，2013）和匹配人与技术模型（谢勒，2002）。

 DESCRIPTION (provided by applicant): The promise of brain-computer interfaces (BCI) for communication is becoming a reality for individuals with severe speech and physical impairments (SSPI) who cannot rely on speech or writing to express themselves. While the majority of research efforts are devoted to technology development to address problems of stability, reliability and/or classification, clinical and behavioral challenges are becoming more apparent as individuals with SSPI and their family/care teams assess the systems during novice or long-term trials. The objective of the RSVP Keyboard(tm) BCI translational research team is to address the clinical challenges raised during functional BCI use with innovative engineering design, thereby enhancing the potential of this novel assistive technology. Four specific aims are proposed: (1) to develop a BCI Communication Application Suite (BCI-CAS) that offers a set of language modules to people with SSPI that can meet their language/literacy skills; (2) to develop improved statistical signal models for personalized feature extraction, artifact/interference handling, and robust, accurate intent evidence extraction from physiologic signals; (3) to develop improved language models and stimulus sequence optimization methods; and (4) to evaluate cognitive variables that affect learning and performance of the BCI-CAS. Five language modules are proposed that rely on a multimodal evidence fusion framework for model-based context-aware optimal intent inference: RSVP Keyboard(tm) generative spelling; RSVP texting; RSVP in-context typing; RSVP in-context icon typing; and binary yes/no responses with SSVEPs. Usability data on the current RSVP Keyboard(tm) and SSVEP system drive all proposed aims. Users select a language module, and the BCI system optimizes performance for each individual based on user adaptation, intent inference, and personalized language modeling. A unique simulation function drives individualization of system parameters. The robustness of the BCI customization efforts are evaluated continually by adults with SSPI and neurotypical controls in an iterative fashion. The effect of three intervention programs that address the cognitive construct of attention (process-specific attention training, mindfulness meditation training and novel stimulus presentations) will be implemented through hypothesis-driven single subject designs. Thirty participants, ages 21 years and older with SSPI will be included in home-based interventions. By measuring information transfer rate (ITR), user satisfaction, and intrinsic user factors, we will identify learning strategies that influence BCI sill acquisition and performance for adults with neurodegenerative or neurodevelopmental conditions. The translational teams include (1) signal processing (Erdogmus); (2) clinical neurophysiology (Oken); (3) natural language processing (Bedrick/Gorman); and (4) assistive technology (Fried-Oken). We continue to rely on a solid Bayesian foundation and theoretical frameworks: ICF disability classification (WHO, 2001), the AAC model of participation (Beukelman & Mirenda, 2013) and the Matching Person to Technology Model (Scherer, 2002).