Zhang Yuanpeng, Zhou Ziyuan, Bai Heming, Liu Wei, Wang Li
Department of Medical Informatics of Medical (Nursing) school, Nantong University, Nantong, China.
Research Center for Intelligence Information Technology, Nantong University, Nantong, China.
Front Neurosci. 2020 Jun 11;14:496. doi: 10.3389/fnins.2020.00496. eCollection 2020.
To recognize abnormal electroencephalogram (EEG) signals for epileptics, in this study, we proposed an online selective transfer TSK fuzzy classifier underlying joint distribution adaption and manifold regularization. Compared with most of the existing transfer classifiers, our classifier has its own characteristics: (1) the labeled EEG epochs from the source domain cannot accurately represent the primary EEG epochs in the target domain. Our classifier can make use of very few calibration data in the target domain to induce the target predictive function. (2) A joint distribution adaption is used to minimize the marginal distribution distance and the conditional distribution distance between the source domain and the target domain. (3) Clustering techniques are used to select source domains so that the computational complexity of our classifier is reduced. We construct six transfer scenarios based on the original EEG signals provided by the Bonn University to verify the performance of our classifier and introduce four baselines and a transfer support vector machine (SVM) for benchmarking studies. Experimental results indicate that our classifier wins the best performance and is not very sensitive to its parameters.
为了识别癫痫患者的异常脑电图（EEG）信号，在本研究中，我们提出了一种基于联合分布自适应和流形正则化的在线选择性转移TSK模糊分类器。与大多数现有的转移分类器相比，我们的分类器有其自身特点：（1）来自源域的标记EEG片段不能准确代表目标域中的主要EEG片段。我们的分类器可以利用目标域中极少的校准数据来诱导目标预测函数。（2）使用联合分布自适应来最小化源域和目标域之间的边缘分布距离和条件分布距离。（3）使用聚类技术来选择源域，从而降低我们分类器的计算复杂度。我们基于波恩大学提供的原始EEG信号构建了六种转移场景，以验证我们分类器的性能，并引入四个基线和一个转移支持向量机（SVM）进行基准研究。实验结果表明，我们的分类器具有最佳性能，并且对其参数不是非常敏感。
