Clinic Interactions of a Brain-Computer Interface for Communication

 描述(申请人提供)：脑机接口(BCI)用于交流的承诺正成为严重言语和身体障碍(SSPI)患者的现实，他们不能依靠语言或文字来表达自己。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，但随着SSPI患者及其家人/护理团队在新手或长期试验期间对系统进行评估，临床和行为挑战变得更加明显。RSVP Keyboard(Tm)脑-机接口翻译研究团队的目标是用创新的工程设计解决功能性脑-机接口使用过程中提出的临床挑战，从而增强这一新型辅助技术的潜力。提出了四个具体的目标：(1)开发一套BCI通信应用套件(BCI-CAS)，为SSPI患者提供一套满足其语言/识字技能的语言模块；(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稳健、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；以及(4)评估影响BCI-CAS学习和表现的认知变量。提出了五个语言模块，它们依赖于基于模型的上下文感知最佳意图推理的多模式证据融合框架：RSVP Keyboard(Tm)生成拼写；RSVP文本；RSVP上下文打字；RSVP上下文图标打字；以及带有SSVP的二进制是/否响应。当前RSVP Keyboard(TM)和SSVEP系统上的可用性数据驱动所有提议的目标。用户选择一个语言模块，BCI系统根据用户适应、意图推理和个性化语言建模来优化每个人的表现。独特的模拟功能推动了系统参数的个性化。患有SSPI和神经典型控制的成年人以迭代的方式不断评估BCI定制工作的稳健性。针对注意的认知结构的三个干预计划(特定过程的注意训练、正念冥想训练和新颖的刺激呈现)的效果将通过假设驱动的单一受试者设计来实施。30名21岁及以上的SSPI患者将被纳入基于家庭的干预措施。通过测量信息传输率(ITR)、用户满意度和固有的用户因素，我们将确定影响患有神经退行性或神经发育疾病的成年人的脑-机接口习得和表现的学习策略。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)；以及(4)辅助技术(Fry-Oken)。我们继续依靠坚实的贝叶斯基础和理论框架：ICF残疾分类(WHO，2001)、AAC参与模式(Beukelman&Mirenda，2013)和人与技术匹配模式(Scherer，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).