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. Itemploys a lightweight deep neural network, EEGNetG. L. et al., 2024; V. J. L. et al., 2018a; W. C. et al., 2024, toclassify 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 latencyand 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.
Liu et al. (Mon,) studied this question.