Automatic artifacts removal of EEG signals using robust principal component analysis
作者： Arjon Turnip
DOI: 10.1109/TIME-E.2014.7011641
关键词: Electroencephalography 、 Robust principal component analysis 、 Artificial intelligence 、 Preprocessor 、 Pattern recognition 、 Principal component analysis 、 Linear combination 、 Mathematics 、 Component analysis 、 Robustness (computer science) 、 Band-pass filter
摘要: Analysis of EEG activity usually raises the problem differentiating between genuine and that which is introduced through a variety external influence. These artifacts may affect outcome recording. In this paper, wavelet denoising band pass filter for preprocessing robustprincipal component analysis algorithm extraction are proposed to remove artifacts. The designed adaptively derive relatively small number decorrelated linear combinations set random zero-mean variables while retaining as much information from original possible. method was tested in real records acquired eight subjects. experimental result show can effectively all
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