Artifacts Removal of EEG Signals using Adaptive Principal Component Analysis

摘要

Analysis of EEG activity usually raises the problem of differentiating between genuine EEG activity 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 an adaptive principal component analysis based recursive least squares 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.