  Alternative data representations are powerful tools that augment the
performance of downstream models. However, there is an abundance of such
representations within the machine learning toolbox, and the field lacks a
comparative understanding of the suitability of each representation method.
  In this paper, we propose artifact detection and classification within EEG
data as a testbed for profiling image-based data representations of time series
data. We then evaluate eleven popular deep learning architectures on each of
six commonly-used representation methods.
  We find that, while the choice of representation entails a choice within the
tradeoff between bias and variance, certain representations are practically
more effective in highlighting features which increase the signal-to-noise
ratio of the data. We present our results on EEG data, and open-source our
testing framework to enable future comparative analyses in this vein.
