引入迁移学习的癫痫EEG 信号自适应识别

doi:10.3969/j.issn.1000-3428.2015.06.029

摘要/Abstract

摘要：

在脑电图(EEG)信号识别中,EEG 信号的采样环境、病人状态的多样性导致分类器训练所用的源域与分类器测试所用的目标域不匹配,分类器在目标域上表现不佳。为此,引入邻域适应策略,提出一种基于子空间相似度的改进主成分分析特征提取方法(SSM-PCA),在选择主成分时,考虑源域和目标域数据的几何和统计特性,并结合迁移学习分类器大间隔投射迁移支持向量机(LMPROJ),给出以SSM-PCA 为基础的LMPROJ 分类识别方法。实验结果表明,与结合PCA 特征抽取技术和K 近邻分类器实现的识别方法相比,该方法在识别正确率方面得到较大提升。

关键词: 特征迁移, 迁移学习, 脑电图信号, 特征提取, 分布多样性, 主成分分析

Abstract:

Many practical applications for epilepsy detection,the diversity of the health status of epilepsy patient and the timing of Electroencephalogram(EEG) signal measurements lead to the mismatching between the source domain used for classifier trained and target domain used for testing. The classifiers usually do not perform well on the target domain. In order to overcome this shortcoming,an improved Principal Component Analysis(PCA) feature extraction method called Subspace Similarity Measure Based Principal Component Analysis (SSM-PCA) is proposed,and a new classification method,named as SSM-PCA-LMPROJ is proposed by integrating SSM-PCA and the classical classifier Large Margin Projected Transductive Support Vector Machine (LMPROJ). Experimental results show that the proposed method has obvious advantages compared with the traditional method,such as the method combining PCA and K Nearest Neighbor (KNN) classifier.

Key words: feature migration, migration learning, Electroencephalogram(EEG) signal, feature extraction, diversity of distribution, Principal Component Analysis(PCA)

中图分类号:

TP391

杨昌健,邓赵红,蒋亦樟,王士同. 引入迁移学习的癫痫EEG 信号自适应识别[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2015.06.029.

YANG Changjian,DENG Zhaohong,JIANG Yizhang,WANG Shitong. Adaptive Recognition of Epileptic EEG Signal with Migration Learning[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2015.06.029.

http://www.ecice06.com/CN/Y2015/V41/I6/158

参考文献

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