Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks
Temporally Adaptive Common Spatial Patterns with Deep Convolutional Neural Networks
复制标题
DOI：
10.1109/embc.2019.8857423
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Mahta Mousavi;V. D. Sa
中科院分区：
文献类型：
--
作者：
Mahta Mousavi;V. D. Sa
脑机接口（BCI）系统被提出作为闭锁患者的一种通信手段。一个常见的脑机接口范例是运动想象，其中用户通过想象不同身体部位的运动来控制脑机接口。我们知道，想象不同的身体部位会导致不同频段的事件相关去同步（ERD）。现有的方法，如公共空间模式（CSP）及其改进滤波器组公共空间模式（FB-CSP），旨在寻找对运动图像分类有信息的特征。我们提出的方法是使用卷积神经网络实现常用的滤波器组公共空间模式方法的时间自适应公共空间模式；因此它被称为TA-CSPNN。利用该方法，我们的目标是：(1)实现端到端的特征提取和分类，(2)基于CSP/FBCSP提取相关特征的方式，最后(3)与现有深度学习方法相比，减少可训练参数的数量，以提高在含噪数据（如EEG）中的泛化能力。更重要的是，我们表明这种参数的减少并不影响性能，事实上，训练后的网络对来自某些参与者的数据有更好的泛化。我们在两个数据集上展示了我们的结果，一个是来自BCI竞赛IV的公开数据集2a，另一个是内部的运动图像数据集。
Brain-computer interface (BCI) systems are proposed as a means of communication for locked-in patients. One common BCI paradigm is motor imagery in which the user controls a BCI by imagining movements of different body parts. It is known that imagining different body parts results in event-related desynchronization (ERD) in various frequency bands. Existing methods such as common spatial patterns (CSP) and its refinement filterbank common spatial patterns (FB-CSP) aim at finding features that are informative for classification of the motor imagery class. Our proposed method is a temporally adaptive common spatial patterns implementation of the commonly used filter-bank common spatial patterns method using convolutional neural networks; hence it is called TA-CSPNN. With this method we aim to: (1) make the feature extraction and classification end-to-end, (2) base it on the way CSP/FBCSP extracts relevant features, and finally, (3) reduce the number of trainable parameters compared to existing deep learning methods to improve generalizability in noisy data such as EEG. More importantly, we show that this reduction in parameters does not affect performance and in fact the trained network generalizes better for data from some participants. We show our results on two datasets, one publicly available from BCI Competition IV, dataset 2a and another in-house motor imagery dataset.