Reducing Intraspecies and Interspecies Covariate Shift in Traumatic
  Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment
Manoj Vishwanath Steven Cao Nikil Dutt Amir M. Rahmani Miranda M. Lim Hung Cao
While analytics of sleep electroencephalography (EEG) holds certain
advantages over other methods in clinical applications, high variability across
subjects poses a significant challenge when it comes to deploying machine
learning models for classification tasks in the real world. In such instances,
machine learning models that exhibit exceptional performance on a specific
dataset may not necessarily demonstrate similar proficiency when applied to a
distinct dataset for the same task. The scarcity of high-quality biomedical
data further compounds this challenge, making it difficult to evaluate the
model's generality comprehensively. In this paper, we introduce Transfer
Euclidean Alignment - a transfer learning technique to tackle the problem of
the dearth of human biomedical data for training deep learning models. We
tested the robustness of this transfer learning technique on various rule-based
classical machine learning models as well as the EEGNet-based deep learning
model by evaluating on different datasets, including human and mouse data in a
binary classification task of detecting individuals with versus without
traumatic brain injury (TBI). By demonstrating notable improvements with an
average increase of 14.42% for intraspecies datasets and 5.53% for interspecies
datasets, our findings underscore the importance of the use of transfer
learning to improve the performance of machine learning and deep learning
models when using diverse datasets for training.