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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721009
• 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原型脑机接口（BMI），允许专家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的关键使能器是软件算法，其能够在真实的时间内识别由非侵入性脑电图（EEG）捕获的脑电波的特征。识别的基础是我们独特的适应分析降阶模型，预测动态非定常流模式的时间和空间。 BMI原型作为一个原理证明，突出其设计，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将通过促进VR/AR环境在各种应用中的有效采用来促进专家的行动，例如规划公共安全（例如，防洪减灾），提高工业竞争力（例如，反应器设计和虚拟原型），以及创新的可信赖的人机界面（例如，人类-机器人防御系统）。