SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment

TL;DR

This paper introduces SelectiveFinetuning, a method that improves sleep stage classification across diverse EEG data by selectively aligning source and target domains using Earth Mover Distance, enhancing robustness and transferability.

Contribution

The paper proposes a novel selective finetuning approach with domain alignment via Earth Mover Distance to address domain shifts in sleep EEG classification.

Findings

Outperforms existing methods in robustness across domain shifts

Enhances model accuracy on diverse EEG datasets

Demonstrates improved transfer learning in sleep staging

Abstract

In practical sleep stage classification, a key challenge is the variability of EEG data across different subjects and environments. Differences in physiology, age, health status, and recording conditions can lead to domain shifts between data. These domain shifts often result in decreased model accuracy and reliability, particularly when the model is applied to new data with characteristics different from those it was originally trained on, which is a typical manifestation of negative transfer. To address this, we propose SelectiveFinetuning in this paper. Our method utilizes a pretrained Multi Resolution Convolutional Neural Network (MRCNN) to extract EEG features, capturing the distinctive characteristics of different sleep stages. To mitigate the effect of domain shifts, we introduce a domain aligning mechanism that employs Earth Mover Distance (EMD) to evaluate and select source domain data closely matching the target domain. By finetuning the model with selective source data, our SelectiveFinetuning enhances the model's performance on target domain that exhibits domain shifts compared to the data used for training. Experimental results show that our method outperforms existing baselines, offering greater robustness and adaptability in practical scenarios where data distributions are often unpredictable.