Eye Blink Artifact Removal in EEG Using Tensor Decomposition
Eye Blink Artifact Removal in EEG Using Tensor Decomposition
复制标题
使用张量分解消除脑电图中的眨眼伪影
DOI：
10.1007/978-3-662-44722-2_17
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
V. Megalooikonomou
中科院分区：
文献类型：
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作者：
D. Triantafyllopoulos;V. Megalooikonomou
脑电数据通常被与受试者活动相关的信号污染，即所谓的伪影，这降低了记录中包含的信息。这种附加信息的去除对于改善脑电信号的解释是至关重要的。该方法基于对用连续小波变换构造的张量进行Tucker分解的分析。我们的贡献是一种自动方法，它同时处理包含在脑电记录中的空间、时间和频率信息，以便去除与眨眼相关的信息。该方法与基于矩阵的去除方法进行了比较，在重建误差和保留无伪影信号的纹理方面取得了令人满意的结果。
EEG data are usually contaminated with signals related to subject’s activities, the so called artifacts, which degrade the information contained in recordings. The removal of this additional information is essential to the improvement of EEG signals’ interpretation. The proposed method is based on the analysis, using Tucker decomposition, of a tensor constructed using continuous wavelet transform. Our contribution is an automatic method which processes simultaneously spatial, temporal and frequency information contained in EEG recordings in order to remove eye blink related information. The proposed method is compared with a matrix based removal method and shows promising results regarding reconstruction error and retaining the texture of the artifact free signal.