Department of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland.
Sensors (Basel). 2024 Jan 31;24(3):918. doi: 10.3390/s24030918.
This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.
本研究提出了一种人机交互结合脑机接口（BMI）和障碍物检测系统，通过运动想象远程控制轮式机器人，为那些面临传统车辆操作挑战的人提供了一种潜在的解决方案。这项工作的主要重点是对想象运动和深度放松状态时与心理活动相关的表面 EEG 信号进行分类。此外，这项工作还提出了一种基于图像处理的障碍物检测系统。所实现的系统构成了接口的补充部分。这项工作的主要贡献包括提出了一种适合运动想象分类的改良的 10-20 电极设置，设计了两个卷积神经网络（CNN）模型来对从 16 个 EEG 通道采集的信号进行分类，以及实现了一种基于计算机视觉与脑机接口集成的障碍物检测系统。本研究中开发的模型在 EEG 信号分类方面达到了 83%的准确率。随后，将分类结果用于控制移动机器人的运动。在指定测试轨道上进行的实验表明，机器人可以实时控制。研究结果表明，障碍物检测系统与运动想象分类相结合用于车辆脑机接口控制的可行性，该综合解决方案可以帮助瘫痪患者通过 EEG 安全地控制轮椅，并有效地防止车辆的意外移动。
