论文标题
使用3D Inception Block的卷积神经网络\ newline对上肢运动的分类\ newline
Classification of Upper Limb Movements \newline Using Convolutional Neural Network \newline with 3D Inception Block
论文作者
论文摘要
基于脑电图（EEG）的脑机界面（BMI）可以克服患者的运动缺陷和健康人的现实应用。理想情况下，BMI系统检测到用户运动意图将它们转化为机器人手臂运动的控制信号。在这项研究中，我们朝着用户意图解码方向取得了进步，并成功地将右臂的六个不同到达运动分类为移动执行（ME）。值得注意的是，我们使用机器人手臂运动设计了一个实验环境，并提出了一个卷积神经网络结构（CNN），其构成块具有稳健分类的同一肢体执行运动。结果，我们确认了执行会话的六个不同方向的分类精度为0.45。结果证明，与常规分类模型相比，所提出的架构的性能提高约为6至13％。因此，我们演示了3D Inception CNN体系结构，以促进我的持续解码。
A brain-machine interface (BMI) based on electroencephalography (EEG) can overcome the movement deficits for patients and real-world applications for healthy people. Ideally, the BMI system detects user movement intentions transforms them into a control signal for a robotic arm movement. In this study, we made progress toward user intention decoding and successfully classified six different reaching movements of the right arm in the movement execution (ME). Notably, we designed an experimental environment using robotic arm movement and proposed a convolutional neural network architecture (CNN) with inception block for robust classify executed movements of the same limb. As a result, we confirmed the classification accuracies of six different directions show 0.45 for the executed session. The results proved that the proposed architecture has approximately 6~13% performance increase compared to its conventional classification models. Hence, we demonstrate the 3D inception CNN architecture to contribute to the continuous decoding of ME.
