Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, People's Republic of China.
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, People's Republic of China.
J Neural Eng. 2021 Mar 17;18(3). doi: 10.1088/1741-2552/abe39b.
. Motor imagery electroencephalography (EEG) decoding is a vital technology for the brain-computer interface (BCI) systems and has been widely studied in recent years. However, the original EEG signals usually contain a lot of class-independent information, and the existing motor imagery EEG decoding methods are easily interfered by this irrelevant information, which greatly limits the decoding accuracy of these methods.. To overcome the interference of the class-independent information, a motor imagery EEG decoding method based on feature separation is proposed in this paper. Furthermore, a feature separation network based on adversarial learning (FSNAL) is designed for the feature separation of the original EEG samples. First, the class-related features and class-independent features are separated by the proposed FSNAL framework, and then motor imagery EEG decoding is performed only according to the class-related features to avoid the adverse effects of class-independent features.. To validate the effectiveness of the proposed motor imagery EEG decoding method, we conduct some experiments on two public EEG datasets (the BCI competition IV 2a and 2b datasets). The experimental results comparison between our method and some state-of-the-art methods demonstrates that our motor imagery EEG decoding method outperforms all the compared methods on the two experimental datasets.. Our motor imagery EEG decoding method can alleviate the interference of class-independent features, and it has great application potential for improving the performance of motor imagery BCI systems in the near future.
运动想象脑电（EEG）解码是脑机接口（BCI）系统的一项关键技术，近年来得到了广泛的研究。然而，原始 EEG 信号通常包含大量与类别无关的信息，现有的运动想象 EEG 解码方法很容易受到这些无关信息的干扰，这极大地限制了这些方法的解码精度。为了克服类无关信息的干扰，本文提出了一种基于特征分离的运动想象 EEG 解码方法。此外，还设计了一种基于对抗学习的特征分离网络（FSNAL），用于原始 EEG 样本的特征分离。首先，通过所提出的 FSNAL 框架分离出与类别相关的特征和与类别无关的特征，然后仅根据与类别相关的特征进行运动想象 EEG 解码，以避免与类别无关的特征的不利影响。为了验证所提出的运动想象 EEG 解码方法的有效性，我们在两个公共 EEG 数据集（BCI 竞赛 IV 2a 和 2b 数据集）上进行了一些实验。与一些最先进的方法进行实验结果比较表明，我们的运动想象 EEG 解码方法在两个实验数据集上均优于所有比较方法。我们的运动想象 EEG 解码方法可以减轻类无关特征的干扰，在不久的将来，它具有提高运动想象 BCI 系统性能的巨大应用潜力。
