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.
Alternative data representations are a critical component of machine learning models, as they can significantly enhance their performance. However, the field currently lacks a comprehensive understanding of the suitability of each representation method. This paper addresses this gap by proposing artifact detection and classification within EEG data as a testbed for profiling image-based data representations of time series data.
The authors evaluate eleven popular deep learning architectures on six commonly-used representation methods in order to determine their efficacy. By analyzing EEG data, they are able to determine which representation methods are more effective in increasing the signal-to-noise ratio of the data.
The multi-disciplinary nature of this research is notable. By applying techniques from computer vision (such as image-based data representations) to EEG data analysis, the authors demonstrate how different fields can work together to advance the understanding and application of machine learning. This cross-pollination of ideas and techniques is crucial for the development of more effective models and algorithms.
The results of this study not only provide valuable insights into the suitability of different representation methods for EEG data analysis but also offer a testing framework that can be utilized for future comparative analyses in similar domains. This open-source testing framework further contributes to the multi-disciplinary nature of the research, as it enables researchers from various disciplines to collaborate and further refine the understanding of alternative data representations.
Overall, this research highlights the importance of considering different representation methods and their impact on downstream models. By conducting a comprehensive evaluation, the authors provide a foundation for future studies in EEG data analysis and other related fields. As machine learning continues to advance, it is crucial to have a deeper understanding of the tradeoffs involved in choosing the most appropriate data representation method.