State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China; University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China.
Neural Netw. 2019 Jun;114:67-77. doi: 10.1016/j.neunet.2019.02.009. Epub 2019 Mar 11.
Brain-computer interfaces (BCIs), which control external equipment using cerebral activity, have received considerable attention recently. Translating brain activities measured by electroencephalography (EEG) into correct control commands is a critical problem in this field. Most existing EEG decoding methods separate feature extraction from classification and thus are not robust across different BCI users. In this paper, we propose to learn subject-specific features jointly with the classification rule. We develop a deep convolutional network (ConvNet) to decode EEG signals end-to-end by stacking time-frequency transformation, spatial filtering, and classification together. Our proposed ConvNet implements a joint space-time-frequency feature extraction scheme for EEG decoding. Morlet wavelet-like kernels used in our network significantly reduce the number of parameters compared with classical convolutional kernels and endow the features learned at the corresponding layer with a clear interpretation, i.e. spectral amplitude. We further utilize subject-to-subject weight transfer, which uses parameters of the networks trained for existing subjects to initialize the network for a new subject, to solve the dilemma between a large number of demanded data for training deep ConvNets and small labeled data collected in BCIs. The proposed approach is evaluated on three public data sets, obtaining superior classification performance compared with the state-of-the-art methods.
脑-机接口（BCI）利用大脑活动来控制外部设备，最近受到了广泛关注。将脑电图（EEG）测量的大脑活动转化为正确的控制命令是该领域的一个关键问题。大多数现有的 EEG 解码方法将特征提取与分类分开，因此在不同的 BCI 用户之间不够稳健。在本文中，我们提出了一种联合学习特定于主体的特征和分类规则的方法。我们开发了一个深度卷积网络（ConvNet），通过将时频变换、空间滤波和分类堆叠在一起，实现对 EEG 信号的端到端解码。我们提出的 ConvNet 实现了一种联合时空频特征提取方案，用于 EEG 解码。与经典卷积核相比，我们网络中使用的 Morlet 小波核显著减少了参数数量，并使相应层学习到的特征具有清晰的解释，即谱幅度。我们进一步利用主体间权重转移，利用现有主体训练的网络参数来初始化新主体的网络，解决了训练深度 ConvNet 需要大量数据和 BCI 中收集的小标记数据之间的困境。该方法在三个公共数据集上进行了评估，与最先进的方法相比，获得了优越的分类性能。
