A Portable EEG-Based Sleep Monitoring and Real-Time Feedback System Without Cloud  Infrastructure

Jinsha Liu; Chengyuan Shen; Jianting Cao

doi:10.24297/ijct.v25i.9763

Authors

Jinsha Liu Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
Chengyuan Shen RIKEN Center for Advanced Intelligence Project (AIP), Chuo-ku, Tokyo, Japan 3Keio University, Yokohama City,
Jianting Cao Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan

DOI:

https://doi.org/10.24297/ijct.v25i.9763

Keywords:

EEG, BCI, Sleep Monitoring, Deep learning, Data Processing

Abstract

This paper presents a mobile real-time sleepL. W. et al., 2023; S. Z. et al., 2024; X. M. et al., 2024; Y. E. et al., 2024;
Jirakittayakorn et al., 2024 staging system using EEG signalsA. A. et al., 2023; H. P. et al., 2022; J. K. L. et al.,
2023; P. J. et al., 2023; S. D. et al., 2023; T. L. et al., 2025; X. Z. et al., 2024a collected from the Muse headband. It
employs a lightweight deep neural network, EEGNetG. L. et al., 2024; V. J. L. et al., 2018a; W. C. et al., 2024, to
classify wakefulness, light sleep, and deep sleep. Designed for Android smartphonesS. B. et al., 2017; S. K. et al., 2023;
X. Z. et al., 2024b, EEG signals are transmitted via Bluetooth for local preprocessing and inference, reducing latency
and preserving privacy. Tests with five healthy subjects showed a classification accuracy of 89.4%, closely aligning with results from traditional polysomnography. The system also features sleep-stage-based interventions, such as adaptive white noise playback, enhancing user sleep experience. Compared to conventional EEG devices, the Muse-based system offers greater comfort, portability, and compliance for long-term use. Results highlight the potential of combining consumer-grade EEG and mobile deep learning for accurate real-time sleep monitoring and personalized sleep health managementM. S. et al., 2018; T. Z. et al., 2023; Lai et al., 2018.

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References

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