Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques

摘要

In order to reduce the muscle artifacts in multi-channel pervasiveElectroencephalogram (EEG) signals, we here propose and compare two hybridalgorithms by combining the concept of wavelet packet transform (WPT),empirical mode decomposition (EMD) and Independent Component Analysis (ICA).The signal cleaning performances of WPT-EMD and WPT-ICA algorithms have beencompared using a signal-to-noise ratio (SNR)-like criterion for artifacts. Thealgorithms have been tested on multiple trials of four different artifact casesviz. eye-blinking and muscle artifacts including left and right hand movementand head-shaking.