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Zhenghui Gu

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6 papers

1 author row

JBHI Journal 2026 Journal Article

Efficient Sleep Staging With Bayesian Uncertainty-Guided Active Learning

Tianyou Yu
Rui Huang
Fei Wang
Jun Zhang
Wei Wu
Zhuliang Yu
Yuanqing Li
Jun Xiao

Automated sleep staging is essential for large-scale and home-based sleep monitoring; however, in routine clinical practice, sleep annotation remains largely dependent on experienced experts performing time-consuming and labor-intensive manual scoring. Existing automatic systems often struggle to adapt reliably to new subjects, limiting their clinical adoption and reinforcing the reliance on expert review. This creates a strong demand for adaptive and efficient sleep staging systems that can substantially reduce annotation workload while preserving expert-level accuracy. We propose BayesSleepNet, a novel framework that integrates Bayesian uncertainty quantification with active learning for adaptive sleep staging. BayesSleepNet employs principled Bayesian modeling by placing distributions over network weights and performing Monte Carlo sampling at inference, enabling explicit quantification of model (epistemic) uncertainty. These uncertainty estimates drive a two-stage sample selection strategy that first fine-tunes the model using representative epochs and subsequently prioritizes persistently uncertain samples for expert review. Across four public sleep datasets, BayesSleepNet consistently improves performance—by 7. 60% in accuracy, 8. 27% in macro-F1, and 0. 104 in Cohen's $\kappa$ —while requiring manual annotation of only 20% of data from new subjects. Despite its adaptive learning capability, BayesSleepNet remains computationally lightweight, using substantially fewer parameters than representative high-capacity state-of-the-art models. These results demonstrate the clinical promise of uncertainty-aware active learning as a practical and cost-efficient paradigm for semi-automated sleep staging. Code is available at https://github.com/yuty2009/bayesugal.

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JBHI Journal 2026 Journal Article

MB-STFormer: A Multi-Band Spectral-Temporal Transformer with Efficient Attention for Enhanced EEG-Based Fatigue Detection

Ke Liu
Lilong Sun
Wenlong Wang
Zhenghui Gu
Zhuliang Yu
Wei Wu

Accurate detection of driver fatigue is critical for preventing traffic accidents. Although electroencephalogram (EEG) signals provide a robust physiological indicator of fatigue, effectively capturing their intricate spatiotemporal-spectral dynamics poses significant challenges. In this paper, we propose MB-STFormer, a novel deep neural network designed for EEG-based fatigue detection, which systematically integrates neurophysiological priors into deep feature learning. The proposed MB-STFormer employs a multi-branch frequency-aware module to extract spatiotemporal features from EEG signals, with each branch dedicated to a distinct frequency sub-band. By leveraging adaptive temporal convolution kernel sizes tailored to each sub-band, the model adeptly captures the inherent rhythmic patterns and temporal dynamics unique to different frequency components. Additionally, we introduce an Efficient Additive Attention mechanism to aggregate global contextual information, thereby addressing the over-smoothing of subtle yet critical features often encountered with conventional transformer self-attention mechanisms. Extensive experiments conducted on three publicly available datasets demonstrate that MB-STFormer achieves state-of-the-art performance while maintaining superior interpretability and generalizability. The proposed framework offers a promising solution for real-world fatigue monitoring systems.

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YNIMG Journal 2026 Journal Article

VSSI 2 p -Net: Physics-guided deep unfolding with L 2 p -norm and variation sparsity for EEG source imaging

Luhua Wang
Jun Zhang
Zhenghui Gu
Ke Liu
Wei Wu
Tianyou Yu
Zhuliang Yu
Yuanqing Li

Electroencephalogram (EEG) source imaging (ESI) is highly underdetermined, which poses a long-standing challenge in neuroimaging. Traditional methods typically rely on predefined priors to constrain the solution space; however, the need for manual parameter adjustments often makes it difficult to achieve optimal integration of prior information. Although recent deep learning methods can automatically update parameters in a data-driven manner, their black-box characteristics lead to a lack of interpretability and the need for extensive training sets. To integrate the advantages of these two types of methods, we propose a novel neural network model based on deep unfolding, called variation sparse source imaging network (VSSI 2 p -Net). Specifically, we introduce variation sparsity and ℓ 2, p norm ( 0 < p < 1 ) regularization into the model of the ESI problem and utilize the Alternating Direction Method of Multipliers (ADMM) to iteratively solve this model. Furthermore, by mapping the iterative process into a neural network structure, the proposed VSSI 2 p -Net can optimize all parameters, including the critical p in ℓ 2, p -norm and the variation sparsity operator, in an end-to-end manner with a reasonably sized training set. In this way, VSSI 2 p -Net achieves more flexible prior information integration while retaining the interpretability of traditional methods, so that a more accurate and efficient solution for ESI can be obtained. We compared the performance of VSSI 2 p -Net with several traditional baseline methods and state-of-the-art deep learning methods on synthetic and real datasets. The results show that VSSI 2 p -Net significantly outperforms existing methods in source localization accuracy, spatial range estimation, and imaging speed across various source configurations.

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JBHI Journal 2025 Journal Article

ADMM-ESINet: A Deep Unrolling Network for EEG Extended Source Imaging

Ke Liu
Hang Jiang
Hu Yang
Jun Zhang
Zhenghui Gu
Zhuliang Yu
Yu Zhang
Bin Xiao

Electroencephalography (EEG) source imaging (ESI) methods aim to reconstruct cortical sources from scalp EEG signals, a crucial task for understanding the normal brain as well as brain disorders. Traditional model-driven ESI methods face challenges in real-time reconstruction, while deep neural network (DNN)-based ESI methods often struggle with generalization to new data. To address these issues, we propose ADMM-ESINet, a novel deep unfolding neural network for robust and efficient reconstruction of EEG extended sources. ADMM-ESINet leverages a structured sparsity constraint within a regularization framework and employs the Alternating Direction Method of Multipliers (ADMM) to achieve iterative solutions. By unrolling the ADMM algorithm into a cascaded network architecture, ADMM-ESINet effectively integrates prior knowledge, enabling end-to-end, real-time ESI. Crucially, both the regularization parameters and the spatial transform operator are learned directly from the training data. Numerical results demonstrate that ADMM-ESINet surpasses traditional DNN-based methods in generalization ability and accurately reconstructs the location, extent, and temporal dynamics of extended sources, establishing ADMM-ESINet as a promising method for real-time ESI.

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YNIMG Journal 2016 Journal Article

Bayesian electromagnetic spatio-temporal imaging of extended sources with Markov Random Field and temporal basis expansion

Ke Liu
Zhu Liang Yu
Wei Wu
Zhenghui Gu
Yuanqing Li
Srikantan Nagarajan

Estimating the locations and spatial extents of brain sources poses a long-standing challenge for electroencephalography and magnetoencephalography (E/MEG) source imaging. In the present work, a novel source imaging method, Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources (BESTIES), which is built upon a Bayesian framework that determines the spatio-temporal smoothness of source activities in a fully data-driven fashion, is proposed to address this challenge. In particular, a Markov Random Field (MRF), which can precisely capture local cortical interactions, is employed to characterize the spatial smoothness of source activities, the temporal dynamics of which are modeled by a set of temporal basis functions (TBFs). Crucially, all of the unknowns in the MRF and TBF models are learned from the data. To accomplish model inference efficiently on high-resolution source spaces, a scalable algorithm is developed to approximate the posterior distribution of the source activities, which is based on the variational Bayesian inference and convex analysis. The performance of BESTIES is assessed using both simulated and actual human E/MEG data. Compared with L 2-norm constrained methods, BESTIES is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the MRF, BESTIES also overcomes the drawback of over-focal estimates in sparse constrained methods.

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JBHI Journal 2015 Journal Article

Energy-Efficient ECG Compression on Wireless Biosensors via Minimal Coherence Sensing and Weighted <formula formulatype="inline"><tex Notation="TeX">$\ell_1$</tex></formula> Minimization Reconstruction

Jun Zhang
Zhenghui Gu
Zhu Liang Yu
Yuanqing Li

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ 1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

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