论文标题
一种新的方法来通过估计来自EEG信号的肌肉活动模式来对自然掌握动作进行分类
A novel approach to classify natural grasp actions by estimating muscle activity patterns from EEG signals
论文作者
论文摘要
开发基于脑电图（EEG）的大脑计算机界面（BCI）系统具有挑战性。在这项研究中，我们分析了脑电图的自然掌握作用。十个健康的受试者参加了这项实验。他们执行并想象了三个持续的掌握动作。我们提出了一种新颖的方法，该方法估算了脑电图信号的肌肉活动模式，以提高整体分类精度。为了实施，我们同时记录了脑电图和肌电图（EMG）。使用来自EEG信号的估计模式的相似性与EMG信号的活动模式进行比较，比竞争方法更高的分类精度。结果，我们获得了实际移动的平均分类精度为63.89（$ \ pm $ 7.54）％，运动成像的平均分类精度为46.96（$ \ pm $ 15.30）。这些分别比竞争模型的结果高21.59％和5.66％。该结果令人鼓舞，并且提出的方法可能会在未来的应用中使用，例如BCI驱动的机器人控制来处理各种日常使用对象。
Developing electroencephalogram (EEG) based brain-computer interface (BCI) systems is challenging. In this study, we analyzed natural grasp actions from EEG. Ten healthy subjects participated in this experiment. They executed and imagined three sustained grasp actions. We proposed a novel approach which estimates muscle activity patterns from EEG signals to improve the overall classification accuracy. For implementation, we have recorded EEG and electromyogram (EMG) simultaneously. Using the similarity of the estimated pattern from EEG signals compare to the activity pattern from EMG signals showed higher classification accuracy than competitive methods. As a result, we obtained the average classification accuracy of 63.89($\pm$7.54)% for actual movement and 46.96($\pm$15.30)% for motor imagery. These are 21.59% and 5.66% higher than the result of the competitive model, respectively. This result is encouraging, and the proposed method could potentially be used in future applications, such as a BCI-driven robot control for handling various daily use objects.
