论文标题
基于新型数据增强策略的脑电图信号的掌握动作解码
Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy
论文作者
论文摘要
基于脑电图（EEG）的大脑计算机界面（BCI）系统是用于神经假体等临床目的的有用工具。在这项研究中，我们收集了与GRASP运动有关的EEG信号。五名健康受试者参加了这项实验。他们执行并想象了五种持续的策略。我们提出了一种新的数据增强方法，该方法使用从肌电图（EMG）信号分析获得的标签来增加训练数据量。对于实施，我们同时记录了脑电图和EMG。与其他竞争对手相比，对原始脑电图数据的数据增加得出的分类精度更高。结果，我们获得了电动机执行（ME）的平均分类精度为52.49％，运动成像（MI）的平均分类精度为40.36％。这些分别比可比方法的结果分别高9.30％和6.19％。此外，提出的方法可以最大程度地减少对校准会话的需求，从而降低了大多数BCI的实用性。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。
Electroencephalogram (EEG) based brain-computer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49% for motor execution (ME) and 40.36% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. 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.
