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

arXiv: https://arxiv.org/abs/2501.03764 · HTML: https://mlsanity.com/paper/2501.03764/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Towards Stable Test-Time Adaptation in Dynamic Wild World](https://mlsanity.com/paper/2302.12400/) (2023)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [Learning to select data for transfer learning with Bayesian Optimization](https://mlsanity.com/paper/1707.05246/) (2017)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification](https://mlsanity.com/paper/2109.01824/) (2021)
- [Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning](https://mlsanity.com/paper/1907.13177/) (2019)
- [Mixed Neural Network Approach for Temporal Sleep Stage Classification](https://mlsanity.com/paper/1610.06421/) (2016)
- [Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce](https://mlsanity.com/paper/1711.08726/) (2017)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)
- [Personalizing deep learning models for automatic sleep staging](https://mlsanity.com/paper/1801.02645/) (2018)
- [Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch](https://mlsanity.com/paper/1904.05945/) (2019)
- [Boost Test-Time Performance with Closed-Loop Inference](https://mlsanity.com/paper/2203.10853/) (2022)
- [BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging](https://mlsanity.com/paper/2411.01589/) (2024)
- [An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance](https://mlsanity.com/paper/2304.14920/) (2023)
- [EEG-based Sleep Staging with Hybrid Attention](https://mlsanity.com/paper/2305.09543/) (2023)

## Built on by (most cited first, 1 of 1)

- [ULW-SleepNet: An Ultra-Lightweight Network for Multimodal Sleep Stage Scoring](https://mlsanity.com/paper/2602.23852/) (2026)

## Abstract (arXiv)

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.