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A Novel Transfer Enhanced -Expansion Move Learning Model for EEG Signals
Scientific Programming ( IF 1.672 ) Pub Date : 2021-04-24 , DOI: 10.1155/2021/9957845
Jiangwei Cai 1 , Lu Zhao 1 , Anqi Bi 1
Affiliation  

In this paper, we focus on recognizing epileptic seizure from scant EEG signals and propose a novel transfer enhanced -expansion move (TrEEM) learning model. This framework implants transfer learning into the exemplar-based clustering model to improve the utilization rate of EEG signals. Starting from Bayesian probability theory, by leveraging Kullback-Leibler distance, we measure the similarity relationship between source and target data. Furthermore, we embed this relationship into the calculation of similarity matrix involved in the exemplar-based clustering model. Then we sum up a new objective function and study this new TrEEM scheme earnestly. We optimize the proposed TrEEM model by borrowing the mechanism utilized in EEM. In contrast to other machine learning models, experiments based on synthetic and real-world EEG datasets show that the performance of the proposed TrEEM is very promising.

中文翻译:

一种新型的脑电信号传递增强-扩展移动学习模型

在本文中,我们着重于从少量的EEG信号中识别癫痫性癫痫发作,并提出了一种新型的转移增强-扩展移动(TrEEM)学习模型。该框架将转移学习植入基于示例的聚类模型中,以提高EEG信号的利用率。从贝叶斯概率理论出发,利用Kullback-Leibler距离,我们可以测量源数据和目标数据之间的相似关系。此外,我们将此关系嵌入到基于示例的聚类模型中的相似度矩阵的计算中。然后,我们总结了一个新的目标函数,并认真研究了这个新的TrEEM方案。我们通过借鉴EEM中使用的机制来优化建议的TrEEM模型。与其他机器学习模型相反,基于合成和真实世界EEG数据集的实验表明,提出的TrEEM的性能非常有前途。
更新日期:2021-04-24
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