论文标题
基于端到端角色分配的卷积神经网络的高维运动图像任务的分类
Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network
论文作者
论文摘要
大脑计算机接口（BCI）提供了用户和外部设备之间的直接通信途径。脑电图（EEG）运动成像（MI）范式在非侵入性BCI中广泛使用，以获得编码信号，其中包含移动执行的用户意图。但是，脑电图具有复杂和非平稳性，导致解码性能不足。通过想象单臂的众多运动，可以在没有人工命令匹配的情况下提高解码性能。在这项研究中，我们收集了直观的脑电图数据包含9个受试者的单臂运动的9种不同类型的运动。我们提出了一个端到端角色分配的卷积神经网络（ERA-CNN），该网络通过采用层次CNN体系结构的原理来考虑每个上肢区域的歧视性特征。所提出的模型优于3级，5级和两种不同类型的7类分类任务的先前方法。因此，我们仅使用ERA-CNN使用具有稳健性能的EEG信号来证明用户意图解码的可能性。
A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-to-end role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using an ERA-CNN.
