Jiang Linzhi, Liu Shuyu, Ma Zhengming, Lei Wenjie, Chen Cheng
School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China.
Public Experimental Teaching Center, Sun Yat-sen University, Guangzhou 510006, China.
Entropy (Basel). 2022 Jan 27;24(2):195. doi: 10.3390/e24020195.
Brain-computer interface (BCI) technology allows people with disabilities to communicate with the physical environment. One of the most promising signals is the non-invasive electroencephalogram (EEG) signal. However, due to the non-stationary nature of EEGs, a subject's signal may change over time, which poses a challenge for models that work across time. Recently, domain adaptive learning (DAL) has shown its superior performance in various classification tasks. In this paper, we propose a regularized reproducing kernel Hilbert space (RKHS) subspace learning algorithm with K-nearest neighbors (KNNs) as a classifier for the task of motion imagery signal classification. First, we reformulate the framework of RKHS subspace learning with a rigorous mathematical inference. Secondly, since the commonly used maximum mean difference (MMD) criterion measures the distribution variance based on the mean value only and ignores the local information of the distribution, a regularization term of source domain linear discriminant analysis (SLDA) is proposed for the first time, which reduces the variance of similar data and increases the variance of dissimilar data to optimize the distribution of source domain data. Finally, the RKHS subspace framework was constructed sparsely considering the sensitivity of the BCI data. We test the proposed algorithm in this paper, first on four standard datasets, and the experimental results show that the other baseline algorithms improve the average accuracy by 2-9% after adding SLDA. In the motion imagery classification experiments, the average accuracy of our algorithm is 3% higher than the other algorithms, demonstrating the adaptability and effectiveness of the proposed algorithm.
脑机接口（BCI）技术使残疾人能够与物理环境进行通信。最有前景的信号之一是非侵入性脑电图（EEG）信号。然而，由于脑电图的非平稳特性，受试者的信号可能随时间变化，这给跨时间工作的模型带来了挑战。最近，域自适应学习（DAL）在各种分类任务中显示出其卓越的性能。在本文中，我们提出了一种正则化再生核希尔伯特空间（RKHS）子空间学习算法，该算法以K近邻（KNN）作为运动想象信号分类任务的分类器。首先，我们通过严格的数学推理重新构建了RKHS子空间学习的框架。其次，由于常用的最大均值差异（MMD）准则仅基于均值来衡量分布方差，而忽略了分布的局部信息，我们首次提出了源域线性判别分析（SLDA）的正则化项，它减小了相似数据的方差，增大了不相似数据的方差，以优化源域数据的分布。最后，考虑到BCI数据的敏感性，稀疏地构建了RKHS子空间框架。我们在本文中对所提出的算法进行了测试，首先在四个标准数据集上进行测试，实验结果表明，其他基线算法在添加SLDA后平均准确率提高了2 - 9%。在运动想象分类实验中，我们算法的平均准确率比其他算法高3%，证明了所提算法的适应性和有效性。
