School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314019, China.
Sensors (Basel). 2024 Jan 25;24(3):785. doi: 10.3390/s24030785.
With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.
随着人们对自然交互的需求不断增加，已经意识到直观的计算机辅助设计（CAD）交互模式可以降低 CAD 操作的复杂性并提升设计体验。尽管像注视和手势这样的交互模式适用于某些复杂的 CAD 操作，但它们仍然需要人们通过物理方式来表达设计意图。大脑隐含地包含设计意图，并控制相应的身体部位来执行任务。因此，在 CAD 操作中建立大脑与计算机之间的端到端通道作为辅助模式，将允许人们通过思维发送设计意图，并使他们的交互更加直观。本工作专注于一维平移场景，并研究了一种空间视觉意象（SVI）范式，为构建基于脑电图（EEG）的 CAD 操作脑机接口（BCI）提供理论支持。基于对与 SVI 相关的三个空间 EEG 特征（例如，共空间模式、互相关和相干性）的分析，构建了基于多特征融合的 SVI 判别模型。10 名被试的意图判别平均准确率为 86%，最高准确率为 93%。验证了所提出的方法在判别 CAD 对象平移意图方面具有良好的分类性能，是可行的。这项工作进一步证明了 BCI 在自然 CAD 操作中的潜力。
