计算机工程与应用2017，Vol.53Issue(11)：167-171,5.DOI:10.3778/j.issn.1002-8331.1610-0117
稀疏降噪自编码器在IR-BCI的应用研究
Research of IR-BCI based on sparse de-noising autoencoder
摘要
Abstract
To solve the problem of features extraction in Brain Computer Interface(BCI), the paper presents a Sparse De-noising Auto-Encoder(SDAE)based on unsupervised learning theory. This method can learn features of brain electrical signal induced by stimulation and explore the deep features of the raw data. The SDAE, a Sparse Autoencoder(SAE)neu-ral network by adding noise at the preprocessing, can enhance the generalization ability of learning and improve the ro-bustness of the neural network. In the experiments, the multi-channel signals are reassembled firstly, and a sparse feature expression of raw data is built by using the SDAE. Then the Support Vector Machines(SVMs)classify the learned fea-tures. Finally, the classification accuracies are compared with those of optimal-single-channel method. The experimental results show that the classification accuracies of SDAE are superior to the optimal-single-channel method, so the SDAE can extract better features, improve the recognition accuracy of the"imitating reading"BCI, thus the method provides a new way of features extraction and classification on the BCI system.关键词
模拟阅读/脑-机接口/非监督学习/稀疏降噪自编码器/支持向量机Key words
imitating reading/Brain Computer Interface(BCI)/unsupervised learning/Sparse De-noising Auto-Encoder (SDAE)/Support Vector Machines(SVMs)分类
信息技术与安全科学引用本文复制引用
赵瑞娟,官金安,谢国栋..稀疏降噪自编码器在IR-BCI的应用研究[J].计算机工程与应用,2017,53(11):167-171,5.基金项目
国家自然科学基金(No.91120017,No.81271659) （No.91120017,No.81271659）
中央高校基本科研业务费资助项目(No.CZY13031). （No.CZY13031）
