Investigating novel approaches to the real time classification of non-invasive brain recordings for use in assistive technologies

研究用于辅助技术的非侵入性脑记录实时分类的新方法

• 批准号: 2292677
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2292677
• 关键词: Investigating; novel; approaches; real; time

Brain Computer Interfaces (BCIs) are a form of Assistive Technology (AT) that can to be used by people with severe motor or sensory disability. However, with current non-invasive BCIs there is a need for the user to devote a significant level of attentive effort on the desired task which effectively abolishes the opportunity for them to multitask and engage in any other concurrent interactive behaviour. The need for high levels of attention to be directed the control of BCIs limits the opportunity for such devices to support user independence.BCIs that are intuitive to use and require little user training are highly desirable for use in home environmental control, social interaction and engagement, navigating smart wheelchairs and instructing robot personal assistants.In this project we will further advance our research into non-invasive, real time BCI controllers that exploit scalp recorded electroencephalographic (EEG) signals that can recognise signatures for intended motor actions and that are suited for integration into smart AT controllers with the potential for fast and robust event classification. Real time classification of user initiated brain signatures for event instructions to a BCI will require research on the signals themselves, the signal processing algorithms that are best suited to classification in real time, the computer architectures to achieve this and development of semi-autonomous AT that will enable the user to be less attentive to the BCI/AT itself. Accordingly, the objective of this project is to develop and implement shared (collaborative) control strategies with the aim to reducing cognitive load and attention of users of BCI linked ATs.The project will develop research skills in real time signal capture, processing and classification of human neurophysiological recordings. The implementation of artificial intelligence into an existing model system of an advanced assistive device simulator (electric wheelchair) in order to allow virtual testing of controllers in which the BCI element and the device have shared control based on 1) the robust and rapid detection of intention events (BCI) and 2) the translation of these commands into goal orientated high, level requests (navigation) to the semi-autonomous AT device. The existing simulator will provide the foundation for development of the shared control strategies for this work.The ultimate goal will be to implement these strategies onto an actual AT and test its performance in real world situations.

脑机接口(BCI)是辅助技术(AT)的一种形式，可用于严重运动或感觉残疾的人。然而，对于当前的非侵入性BCI，用户需要在期望的任务上投入大量的注意力，这有效地消除了他们进行多任务和参与任何其他并发交互行为的机会。BCI的控制需要高度的注意力，这限制了这类设备支持用户独立性的机会。BCI使用直观，只需要很少的用户培训，非常适合用于家庭环境控制、社交互动和参与、导航智能轮椅和指导机器人个人助理。在这个项目中，我们将进一步推进非侵入性、实时BCI控制器的研究，这种控制器利用头皮记录的脑电(EEG)信号可以识别预期运动动作的信号，并适合集成到智能AT控制器中，具有快速和强大的事件分类潜力。将用户发起的用于事件指令的脑信号实时分类到BCI将需要研究信号本身、最适合实时分类的信号处理算法、实现这一点的计算机体系结构以及开发使用户能够较少关注BCI/AT本身的半自动AT。因此，该项目的目标是开发和实施共享(协作)控制策略，以降低BCI连接AT用户的认知负荷和注意力。该项目将发展对人类神经生理记录的实时信号捕获、处理和分类的研究技能。将人工智能实现到高级辅助设备模拟器(电动轮椅)的现有模型系统中，以便允许对其中BCI元件和设备共享控制的控制器进行虚拟测试，其中基于1)健壮和快速的意图事件(BCI)检测以及2)将这些命令转换为对半自动AT设备的面向目标的高层请求(导航)。现有的模拟器将为开发这项工作的共享控制策略提供基础，最终目标是将这些策略实施到实际的AT上，并在真实世界的情况下测试其性能。