ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Perception Science
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1670124
This article is part of the Research TopicCausal Self-Supervised Learning in AI: Advancing Perception ScienceView all articles
Causal-Aware Reliability Assessment of Single-Channel EEG for Transformer-Based Sleep Staging
Provisionally accepted
- Anhui University, Hefei, China
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Single-channel EEG-based sleep staging methods are well-suited for wearable applications in home environments, offering a practical solution to reduce the diagnostic burden on clinical institutions and address the growing demand for large-scale sleep monitoring. However, its reliability remains a critical concern compared to multi-channel polysomnography (PSG) used in clinical settings. To address this, we propose a Transformer-based sleep staging model and conduct a systematic investigation into the causal-inspired analysis between EEG channel selection and staging reliability. Our experiments reveal that electrodes positioned over the central brain region yield significantly higher accuracy, macro-F1, and consistency in sleep stage classification compared to those located in frontal or occipital regions. These findings provide causal insights into the spatial determinants of perceptual reliability in EEG-based sleep monitoring, supporting the design of robust wearable systems.
Keywords: Sleep staging, Single-channel EEG, causal learning, transformer, Classification reliability
Received: 21 Jul 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Hu, Yang, Xu and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jingpeng Sun, jingpeng.sun@ahu.edu.cn
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