论文标题
频道优化基于视觉图像的基于自动臂控制在线环境下
Channel Optimized Visual Imagery based Robotic Arm Control under the Online Environment
论文作者
论文摘要
脑电图是一种有效的方法，可以以非侵入性方式在用户和计算机之间提供双向途径。在这项研究中，我们采用了用于控制基于BCI的机器人组的视觉图像数据。随着用户执行任务，视觉图像随着时间的推移而增加了视觉皮层的Alpha频率范围的功率。我们提出了一种深度学习体系结构，以仅使用两个通道来解码视觉图像数据，还研究了两个具有显着分类性能的EEG通道的组合。当使用提出的方法时，使用两个通道在离线实验中使用两个通道的最高分类性能为0.661。同样，使用两个通道（AF3-OZ）在线实验中的最高成功率为0.78。我们的结果提供了使用视觉图像数据控制基于BCI的机器人臂的可能性。
An electroencephalogram is an effective approach that provides a bidirectional pathway between the user and computer in a non-invasive way. In this study, we adopted the visual imagery data for controlling the BCI-based robotic arm. Visual imagery increases the power of the alpha frequency range of the visual cortex over time as the user performs the task. We proposed a deep learning architecture to decode the visual imagery data using only two channels and also we investigated the combination of two EEG channels that has significant classification performance. When using the proposed method, the highest classification performance using two channels in the offline experiment was 0.661. Also, the highest success rate in the online experiment using two channels (AF3-Oz) was 0.78. Our results provide the possibility of controlling the BCI-based robotic arm using visual imagery data.
