稳态视觉诱发脑机接口特征提取方法的研究
阮晓钢，薛坤
基金项目：国家自然科学基金项目(No.61375086) 作者简介：阮晓钢，(1958- )，男，教授，博士生导师，从事机器人、自动控制等研究.

（北京工业大学电子信息与控制工程学院，北京 100022） 5
摘要：为了有效的提取稳态视觉诱发脑机接口(SSVEP-Based Brain-Computer Interface)中的脑电特征，提出一种基于独立成分分析(Independent Component Analysis，ICA)与希尔伯特黄变换（Hilbert-Huang transform，HHT）的特征提取方法。对采集得到的脑电信号进行带通滤波，得到预处理的脑电信号；将滤波后的脑电信号作为ICA的输入，经过ICA实现独立成分的快速获取；引入HHT对独立成分进行经验模态分解（EMD），分解获取固有模态函数（IMF）；通过对IMF的频域分析，即可提取出特征。实验结果表明，本文的方法在稳态视觉诱发脑机接口的特征提取中是可行的，并且有效的去除了脑电信号中的噪声。
关键词：脑电；稳态视觉诱发电位；脑机接口；独立成分分析；经验模态分解；
中图分类号：R318
Research on Feature Extraction of SSVEP-Based Brain-Computer
Ruan Xiaogang, Xue Kun
(School of Electronic Information and Control Engineering，Beijing University of 20 Technology,Beijing 100022,China)
Abstract: In order to extract the feature of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system more efficiently, a method based on independent component analysis (ICA) and Hilbert-Huang transform (HHT) is proposed in this paper. In the method, band-pass filter is applied to preprocess the electroencephalograph (EEG) of SSVEP. Then the independent components are acquired from filtered signals with ICA. Furthermore, HHT is used, and its inputs are the independent components. Thus the intrinsic mode function (IMF) needed is obtained. Finally, frequency domain analysis is applied to analyse IMF. The experiments show that the proposed method is feasible in feature extraction and the noise also can be removed.
Key words: Electroencephalograph; Steady-State Visual Evoked Potential; Brain-Computer Interface；Independent component analysis; Empirical Mode Decomposition