  Electroencephalograms (EEG) are often contaminated by artifacts which make
interpreting them more challenging for clinicians. Hence, automated artifact
recognition systems have the potential to aid the clinical workflow. In this
abstract, we share the first results on applying various machine learning
algorithms to the recently released world's largest open-source artifact
recognition dataset. We envision that these results will serve as a benchmark
for researchers who might work with this dataset in future.
