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首页> 外文会议>2011 30th 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：基于独立成分分析和模糊c均值聚类技术的操作员脑电图自动眼神器抑制

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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：通过独立成分分析（ICA）和模糊c均值（FCM）聚类，从操作者的脑电图上自动抑制人工眼影。首先，将ICA应用于包含9个EEG数据通道和1个电耳廓图（EOG）数据的20s数据。其次，将每个20 s独立分量（IC）划分为10个2 s纪元。并计算了每个时期的五个特征，即小波熵，0至5 Hz频带内的功率，峰度，互信息和相关性。第三，根据FCM聚类的结果，将时期分为EEG或眼神器。然后，将那些被认为是眼神器的组件拒之门外。获得干净的脑电图。结果表明，基于ICA和FCM的方法可以应用于脑电信号在线自动抑制。

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