# Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment

arXiv: https://arxiv.org/abs/2310.02398 · HTML: https://mlsanity.com/paper/2310.02398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 3 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Time Series Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2002.12478/) (2020)
- [Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach](https://mlsanity.com/paper/1808.05464/) (2018)
- [Deep Learning in EEG: Advance of the Last Ten-Year Critical Period](https://mlsanity.com/paper/2011.11128/) (2020)
- [Phase Locking Value revisited: teaching new tricks to an old dog](https://mlsanity.com/paper/1710.08037/) (2017)
- [A Raspberry Pi-based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram](https://mlsanity.com/paper/2101.10869/) (2021)
- [Chimpanzee voice prints? Insights from transfer learning experiments from human voices](https://mlsanity.com/paper/2112.08165/) (2021)

## Abstract (arXiv)

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.