  The recent emergence and success of electroencephalography (EEG) in low-cost
portable devices, has opened the door to a new generation of applications
processing a small number of EEG channels for health monitoring and
brain-computer interfacing. These recordings are, however, contaminated by many
sources of noise degrading the signals of interest, thus compromising the
interpretation of the underlying brain state. In this work, we propose a new
data-driven algorithm to effectively remove ocular and muscular artifacts from
single-channel EEG: the surrogate-based artifact removal (SuBAR). Methods: By
means of the time-frequency analysis of surrogate data, our approach is able to
identify and filter automatically ocular and muscular artifacts embedded in
single-channel EEG. Results: In a comparative study using artificially contami-
nated EEG signals, the efficacy of the algorithm in terms of noise removal and
signal distortion was superior to other traditionally-employed single-channel
EEG denoising techniques: wavelet thresholding and the canonical correlation
analysis combined with an advanced version of the empirical mode decomposition.
Even in the presence of mild and severe artifacts, our artifact removal method
provides a relative error 4 to 5 times lower than traditional techniques.
Significance: In view of these results, the SuBAR method is a promising
solution for mobile environments, such as ambulatory healthcare systems, sleep
stage scoring or anesthesia monitoring, where very few EEG channels or even a
single channel is available.
