Cross-Subject EEG Signal Recognition Using Deep Domain Adaptation Network
Cross-Subject EEG Signal Recognition Using Deep Domain Adaptation Network
复制标题
使用深域适应网络的跨受试者脑电图信号识别
DOI：
10.1109/access.2019.2939288
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Liu, Xuejun
中科院分区：
文献类型：
--
作者：
Hang, Wenlong;Feng, Wei;Liu, Xuejun
收集足够的标记脑电图 (EEG) 数据来为每个受试者构建个体分类器是极其耗时和费力的，特别是对于残疾患者而言。一种可行的方法是使用来自其他受试者（源域）的标记脑电图数据来训练对来自新受试者（目标域）的脑电图数据进行分类的模型。然而，当脑电图数据存在显着的受试者间变异时，使用其他受试者脑电图数据训练的模型可能会降低目标受试者的分类性能。在本文中，为了解释不同主题之间的域转移，我们提出了一种用于跨主题脑电图信号识别的新型深度域适应网络（DDAN）。具体来说，首先采用特殊的端到端卷积神经网络（CNN）从原始脑电图数据中自动提取深层特征。然后，使用最大平均差异（MMD）来最小化源对象和目标对象之间深层特征的分布差异。最后，采用基于中心的判别特征学习（CDFL）方法，迫使深层特征更接近其对应的类中心，并使类间中心更加可分离，从而可以进一步提高目标域脑电数据的识别性能。在公共脑电图数据集上的实验证明了该方法的有效性。本研究有望促进脑电信号处理技术的实用化，扩大其应用范围。
Collecting sufficient labeled electroencephalography (EEG) data to build an individual classifier for each subject is extremely time-consuming and labor-intensive, especially for the disabled patients. A feasible way is to use labeled EEG data from other subjects (source domains) to train a model for classifying EEG data from the new subjects (target domains). However, the model trained using other subjects EEG data may degrade the classification performance of the target subject, when there exists the substantial inter-subject variability of EEG data. In this paper, to account for the domain shift between different subjects, we propose a novel deep domain adaptation network (DDAN) for cross-subject EEG signal recognition. Specifically, a special end-to-end convolutional neural network (CNN) is firstly adopted to automatically extract deep features from the raw EEG data. Then, maximum mean discrepancy (MMD) is used to minimize the distribution discrepancy of deep features between source and target subjects. Finally, a center-based discriminative feature learning (CDFL) method is used to force the deep features closer to their corresponding class centers and make the inter-class centers more separable, so that it is possible to further improve the recognition performance of target domain EEG data. Experiments on public EEG datasets prove the effectiveness of the proposed method. This study may promote the practical use of EEG signal processing technology and expand its application range.