论文标题
用于分析EEG信号的图形卷积神经网络，BCI应用
Graph Convolutional Neural Networks for analysis of EEG signals, BCI application
论文作者
论文摘要
解码的大脑信号引起了许多关注，近年来发现了许多应用，例如大脑计算机界面，使用用户的意图与控制外部设备进行通信，占据了一个新兴领域，具有改变世界的潜力，从康复到人类增强的各种应用。说这是大脑信号分析，尤其是脑电图脑信号分析是一项具有挑战性的任务。随着仅使用原始数据解决问题的深度学习领域的进步和成就，近年来很少有尝试使用深度学习来解决脑电图和其他类型的大脑信号。在这项研究中，我们提出了一种新的损失函数，称为DEEPCSP，将经典的常见空间模式扩展到非线性，可区分的模块，以作为损耗函数，以实施损失函数，以实施无需执行广泛的特征工程的原始信号的终端方式，在原始信号上终止属于不同类别的EEG信号线性可分离的潜在信号。通过最新的深度学习方法的概括，可以在任意结构的图表上进行工作，并引入了损失，我们提出了两个轻重模型来解码EEG信号和携带实验以显示其性能。
Decoding brain signals has gained many attention and has found much applications in recent years such as Brain Computer Interfaces, communicating with controlling external devices using the user's intentions, occupies an emerging field with the potential of changing the world, with diverse applications from rehabilitation to human augmentation. This being said brain signal analysis, EEG brain signal analysis in particular, is a challenging task. With the advances and achievements in the field of deep learning in problem solving with using only raw data, few attempts has been carried in recent years, to apply deep learning to tackle EEG among other types of brain signals. In this study, we propose a novel loss function, called DeepCSP to extend the classical Common Spatial Patterns to a non linear, differentiable module to serve as the loss function to enforce linearly separable latent representations of EEG signals belonging to different classes in an end to end manner on raw signals without the need to perform extensive feature engineering. With recent generalizations of deep learning methods to work on arbitrarily structured graphs and the introduced loss we have proposed two light weight models to decode EEG signals and carried experiments to show their performance.
