Vieira Rodrigo, Moreno Plinio, Vourvopoulos Athanasios
VisLab, Department of Electrical and Computer Engineering, Institute for Systems and Robotics (ISR-Lisboa), Instituto Superior Técnico, Lisbon, Portugal.
LaSEEB, Department of Bioengineering, Institute for Systems and Robotics (ISR-Lisboa), Instituto Superior Técnico, Lisbon, Portugal.
Front Neurorobot. 2024 Dec 3;18:1491721. doi: 10.3389/fnbot.2024.1491721. eCollection 2024.
As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.
随着机器人在各个领域变得不可或缺，改善人机协作至关重要，尤其是在预测人类行为以提高安全性和效率方面。脑电图（EEG）信号提供了一个有前景的解决方案，因为它们可以在运动前一秒多检测到大脑活动，使机器人具备预测能力。本研究探讨了如何利用脑电图的高时间分辨率和现代深度学习技术，将其用于人机交互（HRI）中的动作预测。我们在一个运动想象（MI）数据集上评估了多个深度学习分类模型，准确率高达80.90%。这些结果在一个试点实验中得到了进一步验证，在该实验中，动作在执行前几百毫秒就被准确预测。这项研究证明了将脑电图与深度学习相结合以增强实时协作任务的潜力，为更安全、高效的人机交互铺平了道路。
