论文标题
从低频脑电图对精细手动运动进行深度学习的分类
Deep learning-based classification of fine hand movements from low frequency EEG
论文作者
论文摘要
来自脑电图信号的不同精细移动的分类代表了相关的研究挑战，例如，在大脑计算机界面应用程序以进行运动康复中。在这里，我们分析了两个不同的数据集，其中进行了精细的手动运动（触摸，抓握，手掌和侧面掌握）。我们培训并测试了新提出的卷积神经网络（CNN），并将其分类性能与两个公认的机器学习模型进行了比较，即缩小的LDA和一个随机的森林。与以前的文献相比，我们利用了神经科学领域的知识，并在所谓运动相关的皮质电位（MRCPS）s上训练了CNN模型。它们是低频的脑电图调制，即（0.3，3）Hz，已被证明是编码运动的几种特性，例如抓握类型，力水平和速度。我们表明，CNN在两个数据集中都取得了良好的性能，并且它们与基线模型相似或优越。同样，与基线相比，我们的CNN需要一个更轻松，更快的预处理程序，为在线模式（例如，对于许多大脑计算机接口应用程序）的可能使用铺平了道路。
The classification of different fine hand movements from EEG signals represents a relevant research challenge, e.g., in brain-computer interface applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed convolutional neural network (CNN), and we compared its classification performance into respect to two well-established machine learning models, namely, a shrinked-LDA and a Random Forest. Compared to previous literature, we took advantage of the knowledge of the neuroscience field, and we trained our CNN model on the so-called Movement Related Cortical Potentials (MRCPs)s. They are EEG amplitude modulations at low frequencies, i.e., (0.3, 3) Hz, that have been proved to encode several properties of the movements, e.g., type of grasp, force level and speed. We showed that CNN achieved good performance in both datasets and they were similar or superior to the baseline models. Also, compared to the baseline, our CNN requires a lighter and faster pre-processing procedure, paving the way for its possible use in an online modality, e.g., for many brain-computer interface applications.
