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首页> 外文会议>Chinese Control Conference >Automatic ocular artifact suppression from human operator's EEG based on a combination of independent component analysis and fuzzy c-means clustering techniques
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Automatic ocular artifact suppression from human operator's EEG based on a combination of independent component analysis and fuzzy c-means clustering techniques

机译：基于独立分量分析和模糊C型聚类技术的组合，自动从人类运营商的脑电图自动抑制

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Independent component analysis (ICA) and fuzzy c-means (FCM) clustering were adopted for automatic ocular artifact suppression from operator's electroencephalogram. Firstly, ICA was applied to the 20s data containing nine channels of EEG data and one of electrooculagram (EOG) data. Secondly, each 20s independent component (IC) was partitioned into ten 2 s epochs. And five features of each epoch were calculated, which are wavelet entropy, power in the band between 0 and 5 Hz, kurtosis, mutual information and correlation. Thirdly, the epochs were classified as either EEG or ocular artifact based on the result of FCM clustering. And then components which were recognized as ocular artifact were rejected. Clean EEG was obtained. The result shows that the method based on ICA and FCM can be applied to online automatic ocular artifact suppression from EEG.
机译：采用独立分量分析（ICA）和模糊C-MATION（FCM）聚类用于操作员脑电图的自动眼部伪影抑制。首先，将ICA应用于包含九个脑电图数据和电耦合数据（EOG）数据的20S数据的20S数据。其次，将每个20s独立的组分（IC）分成十个2秒钟。并计算每个时代的五个特征，是小波熵，在0到5 Hz之间的带中的功率，KurtOsis，相互信息和相关性。第三，基于FCM聚类的结果，将时期被归类为脑电图或眼伪影。然后被识别为眼伪像的组分被拒绝。获得清洁脑电图。结果表明，基于ICA和FCM的方法可以应用于EEG的在线自动眼伪像抑制。

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