Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography

Kweon, Young-Seok; Shin, Gi-Hwan; Kwak, Heon-Gyu; Jo, Ha-Na

doi:10.1109/bci60775.2024.10480481

2024 12th International Winter Conference on Brain-Computer Interface (BCI) 2024

DOI: 10.1109/bci60775.2024.10480481

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Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography

Young-Seok Kweon,

Gi-Hwan Shin,

Heon-Gyu Kwak

et al.

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2024

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Physics-Informed Masked Autoencoder for active sparse imaging

McEvoy,

Tafone,

Sua

et al. 2024

Sci Rep

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Imaging technology based on detecting individual photons has seen tremendous progress in recent years, with broad applications in autonomous driving, biomedical imaging, astronomical observation, and more. Comparing with conventional methods, however, it takes much longer time and relies on sparse and noisy photon-counting data to form an image. Here we introduce Physics-Informed Masked Autoencoder (PI-MAE) as a fast and efficient approach for data acquisition and image reconstruction through hardware implementation of the MAE (Masked Autoencoder). We examine its performance on a single-photon LiDAR system when trained on digitally masked MNIST data. Our results show that, with $$1.8\times 10^{-6}$$ 1.8 × 10 - 6 or less detected photons per pulse and down to 9 detected photons per pixel, it achieves high-quality image reconstruction on unseen object classes with 90% physical masking. Our results highlight PI-MAE as a viable hardware accelerator for significantly improving the performance of single-photon imaging systems in photon-starving applications.

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Physics-Informed Masked Autoencoder for active sparse imaging

McEvoy,

Tafone,

Sua

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

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Multi-Signal Reconstruction Using Masked Autoencoder From EEG During Polysomnography

Cited by 1 publication

References 21 publications

Physics-Informed Masked Autoencoder for active sparse imaging

Physics-Informed Masked Autoencoder for active sparse imaging

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