Brain-machine interfaces for effortless interactions with virtual/augmented environments

脑机接口可轻松与虚拟/增强环境交互

• 批准号: 561064-2020
• 负责人: Hu, Yaoping
• 金额: $ 3.64万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=707331
• 关键词: Brain; machine; interfaces; effortless; interactions

The objective of the project is to create a prototype Brain-Machine Interface (BMI), allowing experts to interact effortlessly with 3D objects within computer-based virtual/augmented reality (VR/AR) environments. The key-enabler of the BMI is software algorithms, capable of identifying in real time characteristic features of brainwaves captured by a non-invasive electroencephalogram (EEG). Underlying the identification is our unique adaptation of analytical reduced-order models, which predict dynamic unsteady-flow patterns in time and space. The prototype BMI serves as a proof-of-principle to highlight its design, implementation, and use for reducing cognitive workload to promote effortless user interactions. Once improved, the BMI is beneficial for Albertan and Canadian economy and society by catalyzing effective adoption of VR/AR environments for experts' actions in various applications - such as planning public safety (e.g., flood mitigation), boosting industrial competitiveness (e.g., reactor design and virtual prototyping), and innovating trustable human-machine interfaces (e.g., human-robot defense systems).

该项目的目标是创建一个脑机接口 (BMI) 原型，让专家能够在基于计算机的虚拟/增强现实 (VR/AR) 环境中轻松地与 3D 对象进行交互。 BMI 的关键推动者是软件算法，能够实时识别非侵入性脑电图 (EEG) 捕获的脑电波特征。识别的基础是我们对分析降阶模型的独特适应，该模型可以预测时间和空间上的动态非定常流动模式。 原型 BMI 作为原理验证，强调其设计、实现和使用，以减少认知工作量，促进轻松的用户交互。一旦改进，BMI 将促进专家在各种应用中有效采用 VR/AR 环境，例如规划公共安全（例如防洪）、提高工业竞争力（例如反应堆），从而对艾伯塔省和加拿大的经济和社会有利。设计和虚拟原型），以及创新可信赖的人机界面（例如人机防御系统）。