论文标题
使用基于关系网络的BCI转移学习方法从EEG信号中解码运动的想象力和执行
Decoding Movement Imagination and Execution from EEG Signals using BCI-Transfer Learning Method based on Relation Network
论文作者
论文摘要
大脑计算机界面（BCI）不仅用于控制健康人员的外部设备，而且还用于为运动型患者恢复运动功能。解码运动意图是使用大脑信号执行手臂运动任务的最重要方面之一。脑电图（EEG）信号的解码运动执行（ME）在以前的工作中显示出高性能，但是迄今为止，基于运动的想象（MI）范式的意图解码未能达到足够的准确性。在这项研究中，我们专注于一种强大的MI解码方法，并通过对ME和MI范式进行转移学习。我们获取了与ARM有关的3D方向的EEG数据。我们提出了一种基于关系网络（BTRN）体系结构的BCI传输学习方法。与传统作品相比，解码性能的性能最高。我们证实了BTRN体系结构使用MI数据集有助于MI连续解码的可能性。
A brain-computer interface (BCI) is used not only to control external devices for healthy people but also to rehabilitate motor functions for motor-disabled patients. Decoding movement intention is one of the most significant aspects for performing arm movement tasks using brain signals. Decoding movement execution (ME) from electroencephalogram (EEG) signals have shown high performance in previous works, however movement imagination (MI) paradigm-based intention decoding has so far failed to achieve sufficient accuracy. In this study, we focused on a robust MI decoding method with transfer learning for the ME and MI paradigm. We acquired EEG data related to arm reaching for 3D directions. We proposed a BCI-transfer learning method based on a Relation network (BTRN) architecture. Decoding performances showed the highest performance compared to conventional works. We confirmed the possibility of the BTRN architecture to contribute to continuous decoding of MI using ME datasets.
