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

TL;DR

This paper presents a deep transfer learning model for EEG-based BCI that reduces data collection needs per user by leveraging multi-subject training and transfer learning, improving practicality for clinical and consumer use.

Contribution

The paper introduces a novel deep transfer learning approach for BCI that significantly decreases individual data requirements, enhancing the feasibility of practical BCI applications.

Findings

Transfer learning reduces data collection per subject.

Benchmark results show improved performance over traditional methods.

Model demonstrates potential for real-world BCI deployment.

Abstract

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.