  Electroencephalogram (EEG) signals are often corrupted with unintended
artifacts which need to be removed for extracting meaningful clinical
information from them. Typically a priori knowledge of the nature of the
artifacts is needed for such purpose. Artifact contamination of EEG is even
more prominent for pervasive EEG systems where the subjects are free to move
and thereby introducing a wide variety of motion-related artifacts. This makes
hard to get a priori knowledge about their characteristics rendering
conventional artifact removal techniques often ineffective. In this paper, we
explore the performance of two hybrid artifact removal algorithms: Wavelet
packet transform followed by Independent Component Analysis (WPTICA) and
Wavelet Packet Transform followed by Empirical Mode Decomposition (WPTEMD) in
pervasive EEG recording scenario, assuming existence of no a priori knowledge
about the artifacts and compare their performance with two existing artifact
removal algorithms. Artifact cleaning performance has been measured using Root
Mean Square Error (RMSE) and Artifact to Signal Ratio (ASR) - an index similar
to traditional Signal to Noise Ratio (SNR), and also by observing normalized
power distribution topography over the scalp. Comparison has been made first
using semi-simulated signals and then with real experimentally acquired EEG
data with commercially available 19-channel pervasive EEG system Enobio
corrupted by eight types of artifact. Our explorations show that WPTEMD
consistently gives best artifact cleaning performance not only in
semi-simulated scenario but also in the case of real EEG data containing
artifacts.
