发表机构
IIT Gandhinagar; Ulster University（印度理工学院甘地纳加尔分校; 阿尔斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合眼动追踪与运动想象的异步混合BCI范式，通过视觉注视增强神经稳定性，实现高精度、鲁棒的多命令决策通信。
AI 中文摘要
非侵入式脑机接口（BCI）和眼动追踪技术提供了有前景的通信途径；然而，基于运动想象（MI）的BCI常常面临低可区分性和高个体间差异的问题。为缓解这些问题，本研究探讨了视觉注视对独立MI和混合MI-眼动追踪系统中神经响应稳定性的影响。随后，我们提出了一种新颖的异步混合范式，通过利用眼动追踪进行直接选择，随后进行基于MI的确认，从而简化用户意图，显著减少了传统系统所需操作步骤。该范式在16通道脑电图系统上对15名健康参与者进行了评估。结果表明，MI相关信息主要定位于运动皮层区域，有限通道配置（SVM：0.58）达到了与全导联配置（SVM：0.54）相当的性能。混合MI范式进一步优于传统MI，在所有通道配置下实现了高达100%的准确率，并具有更强的鲁棒性。我们的研究结果表明，视觉注视增强了神经响应稳定性，而将眼动追踪与MI相结合，能够开发出适用于实际应用的可靠、可扩展的多命令BCI系统。
英文摘要
Non-invasive brain-computer interfaces (BCIs) and eye-tracking technologies offer promising communication pathways; however, motor imagery (MI)-based BCIs often suffer from low discriminability and high inter-subject variability. To mitigate these issues, this study investigates the impact of visual fixation on neural response stability in both standalone MI and hybrid MI-eye tracking systems. We then propose a novel asynchronous hybrid paradigm that streamlines user intent by utilising eye-tracking for direct selection, followed by MI-based confirmation, significantly reducing the operational steps required by conventional systems. The paradigm was evaluated with 15 healthy participants using a 16-channel EEG system. Results show that MI-related information is predominantly localised within motor cortex regions, with limited-channel configurations (SVM: 0.58) achieving performance comparable to full-montage setups (SVM: 0.54). The hybrid MI paradigm further outperforms conventional MI, achieving up to 100% accuracy with greater robustness across all channel configurations. Our findings indicate that visual fixation enhances neural response stability, while integrating eye-tracking with MI enables the development of reliable, scalable multi-command BCI systems suitable for real-world applications.
Comments6 pages, 5 figures, selected to be presented at Brain-Machine Interface (BMI) Systems Session, IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2026)
