机械臂抓取任务的脑电分类

针对大脑认知完好无损的患者,却患有重度神经肌肉疾病导致肢体行动受限的问题,为使患者重新获取障碍肢体的自主控制能力,本文提出了一种机械臂抓取任务的脑电分类方法对患者进行障碍肢体运动康复训练.首先使用非侵入式脑电技术对运动想象脑电信号进行采集,通过预处理、特征提取以及多尺度特征融合卷积神经网络进行分类识别;最后利用分类模型得到的标签解码成机械臂能够识别的指令,控制机械臂完成特定任务.实验结果表明:实验选取的15名健康受试者运动想象实验采集的脑电数据具有可行性,平均准确率达到了82%以上;为机械臂抓取任务的脑电分类提供了一种新思路.

EEG Classification of Robotic Arm Grasping Task

Aiming at the problem of limb movement limitation caused by severe neuromuscular disease in patients with intact brain cognition, this study tries to enable patients to autonomously control their impaired limbs again and thus proposes an electroencephalogram (EEG) classification method of robotic arm grasping task to carry out rehabilitation training for patients with impaired limb movement. Firstly, the motor imagery EEG signals are collected by a non-invasive EEG technology, and they are then classified and identified by preprocessing, feature extraction, and convolutional neural networks with multi-scale feature fusion. Finally, the label obtained by the classification model is decoded into instructions that can be recognized by the robotic arm, so as to control the arm to fulfill specific tasks. The experimental results show that the EEG data collected from experimentally selected 15 healthy subjects in the motor imagery experiments are feasible, and the average accuracy rate reaches more than 82%, which provides a new idea for EEG classification of robotic arm grasping task.