Institute of Mechanical Technology, Poznan University of Technology, ul. Piotrowo 3, 60-965 Poznań, Poland.
Sensors (Basel). 2021 Oct 30;21(21):7244. doi: 10.3390/s21217244.
Research focused on signals derived from the human organism is becoming increasingly popular. In this field, a special role is played by brain-computer interfaces based on brainwaves. They are becoming increasingly popular due to the downsizing of EEG signal recording devices and ever-lower set prices. Unfortunately, such systems are substantially limited in terms of the number of generated commands. This especially applies to sets that are not medical devices. This article proposes a hybrid brain-computer system based on the Steady-State Visual Evoked Potential (SSVEP), EOG, eye tracking, and force feedback system. Such an expanded system eliminates many of the particular system shortcomings and provides much better results. The first part of the paper presents information on the methods applied in the hybrid brain-computer system. The presented system was tested in terms of the ability of the operator to place the robot's tip to a designated position. A virtual model of an industrial robot was proposed, which was used in the testing. The tests were repeated on a real-life industrial robot. Positioning accuracy of system was verified with the feedback system both enabled and disabled. The results of tests conducted both on the model and on the real object clearly demonstrate that force feedback improves the positioning accuracy of the robot's tip when controlled by the operator. In addition, the results for the model and the real-life industrial model are very similar. In the next stage, research was carried out on the possibility of sorting items using the BCI system. The research was carried out on a model and a real robot. The results show that it is possible to sort using bio signals from the human body.
研究集中在人体信号上的研究越来越受欢迎。在这个领域，基于脑电波的脑机接口扮演着特殊的角色。由于 EEG 信号记录设备的小型化和不断降低的设定价格，它们越来越受欢迎。然而，这样的系统在生成的命令数量方面受到了很大的限制。这尤其适用于非医疗设备的系统。本文提出了一种基于稳态视觉诱发电位（SSVEP）、眼动追踪和力反馈系统的混合脑机系统。这种扩展的系统消除了许多特定系统的缺点，并提供了更好的结果。本文的第一部分介绍了混合脑机系统中应用的方法信息。该系统在操作员将机器人末端放置到指定位置的能力方面进行了测试。提出了一个工业机器人的虚拟模型，并在测试中使用了该模型。在实际的工业机器人上重复了测试。启用和禁用反馈系统后，验证了系统的定位精度。在模型和实际物体上进行的测试结果清楚地表明，当操作员控制机器人时，力反馈可以提高机器人末端的定位精度。此外，模型和实际工业模型的结果非常相似。在下一阶段，研究了使用 BCI 系统进行物品分类的可能性。研究在模型和实际机器人上进行。结果表明，使用人体生物信号进行分类是可行的。
