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      1. 當前位置:首頁 > 智慧健康列表 > 轉化醫學和循證醫學 > 詳細信息
        來源: | 作者: | 發布時間:2014-4-15 18:22:54

        基金項目:國家自然科學基金項目(No.61375086) 作者簡介:阮曉鋼,(1958- ),男,教授,博士生導師,從事機器人、自動控制等研究.

        (北京工業大學電子信息與控制工程學院,北京 100022) 5
        摘要:為了有效的提取穩態視覺誘發腦機接口(SSVEP-Based Brain-Computer Interface)中的腦電特征,提出一種基于獨立成分分析(Independent Component Analysis,ICA)與希爾伯特黃變換(Hilbert-Huang transform,HHT)的特征提取方法。對采集得到的腦電信號進行帶通濾波,得到預處理的腦電信號;將濾波后的腦電信號作為ICA的輸入,經過ICA實現獨立成分的快速獲取;引入HHT對獨立成分進行經驗模態分解(EMD),分解獲取固有模態函數(IMF);通過對IMF的頻域分析,即可提取出特征。實驗結果表明,本文的方法在穩態視覺誘發腦機接口的特征提取中是可行的,并且有效的去除了腦電信號中的噪聲。
        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