# Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface

arXiv: https://arxiv.org/abs/2301.12322 · HTML: https://mlsanity.com/paper/2301.12322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 29 Jan 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)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020)
- [Beyond Human-Level Accuracy: Computational Challenges in Deep Learning](https://mlsanity.com/paper/1909.01736/) (2019)
- [Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders](https://mlsanity.com/paper/1812.06857/) (2018)
- [Reducing training requirements through evolutionary based dimension reduction and subject transfer](https://mlsanity.com/paper/1602.02237/) (2016)
- [Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach](https://mlsanity.com/paper/1808.05853/) (2018)
- [Applying Transfer Learning To Deep Learned Models For EEG Analysis](https://mlsanity.com/paper/1907.01332/) (2019)

## Abstract (arXiv)

Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagnostic tools. Despite advancements in the field, however, at present consumer and clinical viability remains low. A key reason for this is that many of the existing BCI deployments require substantial data collection per end-user, which can be cumbersome, tedious, and error-prone to collect. We address this challenge via a deep learning model, which, when trained across sufficient data from multiple subjects, offers reasonable performance out-of-the-box, and can be customized to novel subjects via a transfer learning process. We demonstrate the fundamental viability of our approach by repurposing an older but well-curated electroencephalography (EEG) dataset and benchmarking against several common approaches/techniques. We then partition this dataset into a transfer learning benchmark and demonstrate that our approach significantly reduces data collection burden per-subject. This suggests that our model and methodology may yield improvements to BCI technologies and enhance their consumer/clinical viability.