Sara Wilson Kerlin, Saravanan K K
Department of Electrical and Electronics Engineering, University College of Engineering Thirukkuvalai - A Constituent College of Anna University, Thirukkuvalai, Tamil Nadu, India.
Network. 2025 Aug;36(3):1253-1281. doi: 10.1080/0954898X.2025.2453620. Epub 2025 Jan 30.
Brain-controlled robotic arm systems are designed to provide a method of communication and control for individuals with limited mobility or communication abilities. These systems can be beneficial for people who have suffered from a spinal cord injury, stroke, or neurological disease that affects their motor abilities. The ability of a person to control a robotic arm to reach and grasp multiple objects using their brain signals. This technology involves the use of an electroencephalogram (EEG) cap that captures the electrical activity in the user's brain, which is then processed by an artificial intelligent to translate it into commands that control the movements of the robotic arm. With this technology, individuals who are unable to move their limbs due to paralysis or other conditions can still perform daily activities such as feeding themselves, drinking from a glass, or grasping objects. In this paper, we propose an artificial intelligent-based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system. The proposed control strategy consists of threefold process: feature extraction, feature optimization, and control strategy classification. Initially, we design an improved ResNet pre-trained architecture for deep feature extraction from the given EEG signal.
脑控机器人手臂系统旨在为行动能力或沟通能力受限的个体提供一种沟通和控制方法。这些系统对患有脊髓损伤、中风或影响其运动能力的神经疾病的人可能有益。一个人利用其大脑信号控制机器人手臂去够取和抓握多个物体的能力。这项技术涉及使用脑电图（EEG）帽来捕捉用户大脑中的电活动，然后由人工智能进行处理，将其转化为控制机器人手臂运动的指令。借助这项技术，因瘫痪或其他状况而无法移动四肢的个体仍能进行诸如自行进食、从杯子喝水或抓握物体等日常活动。在本文中，我们提出了一种基于人工智能的控制策略，用于使用脑控机器人手臂系统实现对多个物体的够取和抓握。所提出的控制策略包括三个过程：特征提取、特征优化和控制策略分类。最初，我们设计了一种改进的预训练ResNet架构，用于从给定的EEG信号中进行深度特征提取。
