Eyeblink Artifact Suppression from EEG Signal using Lifting Wavelet Transform
نویسندگان
چکیده
In this paper we proposed a technique to remove eye blink artifact from electroencephalogram (EEG) using lifting wavelet transform (LWT). The LWT has been successfully used in eye blink artifact suppression form the recorded electroencephalography (EEG) signals using a data-adaptive subband filtering approach. The LWT is applied to decompose EEG signal into a finite set of subbands. The energy based subband filtering is implemented to separate the lower frequency noise components to clean the EEG signal. The energies of individual subbands respectively for EEG and fGn that of contaminated EEG are compared to derive the energy based threshold for the suppression of eyeblink effects. We adopt two de-noising algorithms based on stationary subspace analysis (SSA), and lifting wavelet transform (LWT) for comparison purpose. Through using contaminated EEG signals from BCI database, we evaluate the artifact correction results by means of SAR and MSE, and conclude that LWT algorithm is the suitable one for de-noising EEG signal.
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