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

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） neural network by adding noise at the preprocessing, can enhance the generalization ability of learning and improve the robustness 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 features. 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.

Key words: imitating reading, Brain Computer Interface（BCI）, unsupervised learning, Sparse De-noising Auto-Encoder（SDAE）, Support Vector Machines（SVMs）