Automatic artifacts removal of EEG signals using robust principal component analysis


Analysis of EEG activity usually raises the problem of differentiating between genuine EEG activity and that which is introduced through a variety of external influence. These artifacts may affect the outcome of the EEG recording. In this paper, wavelet denoising and band pass filter for preprocessing and a robustprincipal component analysis algorithm for extraction are proposed to remove the artifacts. The algorithm is designed to adaptively derive a relatively small number of decorrelated linear combinations of a set of random zero-mean variables while retaining as much of the information from the original variables as possible. The proposed method was tested in real EEG records acquired from eight subjects. The experimental result show that the proposed method can effectively remove the artifacts from all subjects.

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