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.

提出了利用 EEG 数据作为时序数据的基于图像的数据表示方法的测试平台，并对六种常用的表示方法进行了十一种流行的深度学习架构的评估。发现在表示方法的选择中，存在着偏差与方差之间的权衡，然而某些表示方法在突出增加数据的信噪比方面更加有效。通过 EEG 数据呈现了实验结果，并开源了我们的测试框架，以促进未来在这方面进行的比较分析。