论文标题
使用卷积神经网络对单臂任务进行运动图像分类
Motor Imagery Classification of Single-Arm Tasks Using Convolutional Neural Network based on Feature Refining
论文作者
论文摘要
大脑计算机界面（BCI）解码大脑信号以了解用户意图和状态。由于其简单且安全的数据采集过程，脑电图（EEG）通常用于非侵入性BCI。脑电图范式之一，运动图像（MI）通常用于恢复或恢复运动功能，原因是其信号起源。但是，EEG信号是振荡和非平稳信号，使得难以准确收集和分类MI。在这项研究中，我们提出了一个由功率特征精制的卷积神经网络（BFR-CNN），该卷积神经网络由两个卷积块组成，以实现高分类的精度。我们收集了EEG信号来创建MI数据集，其中包含单臂的运动想象。在4级MI任务分类中，所提出的模型优于常规方法。因此，我们仅使用BFR-CNN使用具有稳健性能的EEG信号来证明用户意图的解码是可能的。
Brain-computer interface (BCI) decodes brain signals to understand user intention and status. Because of its simple and safe data acquisition process, electroencephalogram (EEG) is commonly used in non-invasive BCI. One of EEG paradigms, motor imagery (MI) is commonly used for recovery or rehabilitation of motor functions due to its signal origin. However, the EEG signals are an oscillatory and non-stationary signal that makes it difficult to collect and classify MI accurately. In this study, we proposed a band-power feature refining convolutional neural network (BFR-CNN) which is composed of two convolution blocks to achieve high classification accuracy. We collected EEG signals to create MI dataset contained the movement imagination of a single-arm. The proposed model outperforms conventional approaches in 4-class MI tasks classification. Hence, we demonstrate that the decoding of user intention is possible by using only EEG signals with robust performance using BFR-CNN.
