IEEE Transactions on Biomedical Engineering最新文献

Speaker identification in auditory attention decoding (SI-AAD) aims to identify the attended speaker from electroencephalography (EEG) signals. However, its application for hearing-impaired individuals is limited since existing methods rarely consider altered auditory perception from hearing-assistive devices under mixed-speech conditions, compounded by the lack of relevant datasets and difficulties in learning robust EEG-speech correspondences due to weak cross-modal alignment and insufficient feature extraction. Therefore, we construct five mixed-speech AAD datasets (MS-AAD), serving as the first EEG benchmark to simulate typical device-induced acoustic alterations without spatial cues. To enhance modality alignment, we propose a timbre-enhanced latent alignment (TELA) framework that jointly models latent embeddings and perceptual speaker cues via contrastive learning and auxiliary timbre classification. To further improve EEG-based feature extraction, we design FCTNet, a frequency-channel-temporal attention-based EEG encoder that captures rich neural patterns across multiple domains. Experiments on MS-AAD demonstrate that TELA and FCTNet jointly achieve 89.5% SI-AAD accuracy across diverse hearing conditions, highlighting the critical role of device-simulated acoustic dataset design and perceptually guided representation learning with advanced EEG encoding in mixed-speech SI-AAD for hearing-assistive applications.

Objective: Recent outbreaks and pandemics have emphasized the need for safe and reliable viral inactivation methods. The purpose of this work is to develop a simple and effective modeling approach to investigate viral inactivation via microwave absorption mediated by dipolar coupling.

Methods: Leveraging established techniques from the dynamic analysis of structures, a generalized Single-Degree-Of-Freedom (SDOF) model is developed, which is fully consistent with the dipolar resonance mode.

Results: The model can reproduce the main features of dipolar coupling with minimal computational time. Moreover, it allows mimicking the broadening of the resonance range associated with heterogeneous virus size, via Monte Carlo simulations, as well as water induced damping.

Conclusion: The results support the potential role of dipolar coupling for viral inactivation by microwave irradiation in the GHz range. The model can be used to assist in the interpretation of the experimental results, leading to an optimization of the inactivation protocols.

Significance: The proposed approach is versatile and can be extended to describe more complex cases, such as non-spherical geometries and/or non-homogeneous material properties.

Objective: Recent neuroscientific research craves for understanding sophisticated brain networks formed by neuron ensembles across multiple regions. An ideal way to unveil the complex connectome is bidirectionally interacting (simultaneous recording and stimulation) with neurons at high spatiotemporal resolutions. Existing CMOS recording probes cannot provide micro-scale interactions with limited stimulation capability. Although optogenetics can achieve neuron-specific stimulation, conventional methods using optic fibers illuminate a large volume of tissue, resulting in unspecific perturbations. While our previous studies demonstrated micro-LED (μLED)-based optoelectrode for localized stimulation and recording, this work advances them into a fully integrated headstage combining the optoelectrode, CMOS IC, and flexible interposer for miniaturized implementation. The proposed system enables micro-scale interactions with high spatiotemporal precision through densely packed 256-neuron-size recording and 128-soma-size fiber-less opto-stimulation across multiple brain regions.

Methods: Such high resolutions yet wide coverage is achieved by (1) advanced micromachining techniques integrating recording electrodes and μLEDs, (2) micro-second, independent 384-channel interaction via a low-power, area-efficient circuit, and (3) compact and reliable polyimide-cable-based hybrid assembly.

Results: A compact (23.8×28.8 mm2) and lightweight (3.5-gram) headstage achieved the highest reported channel density in area (0.56 channels/mm2) and weight (109.71 channels/gram). A single acute in vivo experiment on a transgenic mouse identified >160 isolated pyramidal neurons and narrow/wide interneurons in the dorsal hippocampus, with local and broad-range effects from focal optogenetic stimulation.

Conclusion and significance: We implemented the hybrid integrated, large-scale opto-electrophysiology interface prototype and verified its feasibility in vivo, representing the first fully integrated platform extending our μLED-based probes into a complete system.

Traditionally, venous stents have been employed to maintain vessel patency in cases of venous obstruction. Recent advancements in stent-electrode technology have broadened their application to include endovascular neural interfaces for neurotechnological purposes within cerebral veins. However, the effects of neointimal hyperplasia on large venous sinuses, particularly the superior sagittal sinus and jugular vein, remain poorly understood. Additionally, concerns such as thrombosis, chronic inflammation, and tissue overgrowth pose challenges for their long-term use as neural interfaces.

Methods: To investigate the impact of venous stenting on blood flow and tissue growth, we utilized Computational Fluid Dynamics (CFD) modeling and animal experiments, assessing blood flow and tissue responses over 28 days.

Results: Our findings revealed a negative power law correlation, with low wall shear stress (WSS) identified as the primary driver of accelerated tissue growth. Unlike the focal narrowing typically observed in stented arteries, venous tissue growth exhibited greater variability. Additionally, the threshold for low WSS that triggered growth was smaller than previously reported in arteries.

Conclusion: This study provides new insights into venous neointimal hyperplasia, emphasizing the need to consider venous-specific responses in stent-electrode design and clinical applications. Nonetheless, potential risks such as thrombosis and inflammatory responses should be further investigated to fully understand the long-term viability of these devices.

Significance: Understanding the biomechanical environment of stents in cerebral veins can guide the development of next-generation neural interfaces and inform clinicians and device developers about potential impacts on long-term outcomes.

Objective: Label-free electrical impedance-based single-cell detection has been widely applied in cell sorting, electrical phenotyping, and monitoring of cell growth status. However, when high-concentration cell suspensions pass through the sensing region simultaneously, coincident events frequently occur, which leads to inaccurate segmentation of cell events and distorted identification of single-cell waveforms. As a result, statistical errors in electrical phenotyping are introduced.

Methods: In this work, we propose a two-step sparse-constrained optimization algorithm based on $ell _{1}$-norm regularization, which addresses this challenge without requiring any structural modification to the microfluidic chip. The raw signal is processed using this two-step framework: first, a waveform detection dictionary is constructed to segment the signal; subsequently, a de-coincidence dictionary is applied to resolve coincident waveforms.

Results: Experimental validation on synthetic data streams demonstrates robust counting accuracy from 2×10⁵ to 5×10⁶ particles/ml (99.9%-98.4%), with only a 5.1% reduction under five levels of additive noise at 2×10⁶ particles/ml. Analysis of polystyrene beads of two sizes and T cells at three concentrations demonstrates enhanced size discrimination, improved statistical accuracy, and consistent counting performance compared with conventional algorithms.

Conclusion: The proposed method effectively segments and decomposes coincident signals into individual cell events by employing sparse optimization techniques.

Significance: This algorithm is well suited for applications that demand accurate counting and classification of cell/particle suspensions across a wide concentration range.

Multivariate cortico-muscular analysis has recently emerged as a promising approach for evaluating the corticospinal neural pathway. However, current multivariate approaches encounter challenges such as high dimensionality and limited sample sizes, thus restricting their further applications. In this paper, we propose a structured and sparse partial least squares coherence algorithm (ssPLSC) to extract shared latent space representations related to cortico-muscular interactions. Our approach leverages an embedded optimization framework by integrating a partial least square (PLS)-based objective function with sparsity and connectivity-based structured constraints, addressing the generalizability, sparsity and spatial structure. To solve the optimization problem, we develop an efficient alternating iterative algorithm within a unified framework and prove its convergence experimentally. Extensive experimental results from one synthetic and several real-world datasets have demonstrated that ssPLSC can achieve competitive or better performance over some representative multivariate cortico-muscular fusion methods, particularly in scenarios characterized by limited sample sizes and high noise levels. This study provides a novel multivariate fusion method for cortico-muscular analysis, offering a potential tool for the evaluation of corticospinal pathway integrity in neurological disorders.

Objective: In this work, we propose a framework for differentiable forward and back-projector that enables scalable, accurate, and memory-efficient gradient computation for rigid motion estimation tasks.

Methods: Unlike existing approaches that rely on auto-differentiation or that are restricted to specific projector types, our method is based on a general analytical gradient formulation for forward/backprojection in the continuous domain. A key insight is that the gradients of both forward and back-projection can be expressed directly in terms of the forward and back-projection operations themselves, providing a unified gradient computation scheme across different projector types. Leveraging this analytical formulation, we develop a discretized implementation with an acceleration strategy that balances computational speed and memory usage.

Results: Simulation studies illustrate the numerical accuracy and computational efficiency of the proposed algorithm. Experiments demonstrates the effectiveness of this approach for multiple X-ray imaging tasks we conducted. In 2D/3D registration, the proposed method achieves $sim$8× speedup over an existing differentiable forward projector while maintaining comparable accuracy. In motion-compensated analytical reconstruction and cone-beam CT geometry calibration, the proposed method enhances image sharpness and structural fidelity on real phantom data while showing significant efficiency advantages over existing gradient-free and gradient-based solutions.

Conclusion: The proposed differentiable projectors enable effective and efficient gradient-based solutions for X-ray imaging tasks requiring rigid motion estimation.

Brain age estimation from structural MRI is an effective approach for detecting abnormal neurodevel opment and neurodegeneration. However, most existing methods produce global biomarkers that lack tissue-level specificity and fail to leverage medical prior knowledge. To address these limitations, we propose T2AgeNet, a dual-path image-text framework for tissue-level brain age estimation that integrates anatomical features with clinical semantics. The framework first segments brain MRI to generate tissue-specific masks, forming the basis for localized age prediction. To further incorporate medical prior knowledge, the model first aligns visual features with personalized clinical descriptions to guide semantic understanding of tissue-level variation. In parallel, it transforms handcrafted aging-related features into textual representations through an auxiliary branch using a large language model, enabling enriched interpretation and representation. We evaluate T2AgeNet on five datasets spanning fetal development, preterm infants, Alzheimer's disease, and autism spectrum disorder. Results demonstrate accurate age estimation across diverse populations. On the OASIS-3 and ABIDE-I datasets, the model further identifies tissue specific structural abnormalities consistent with known neurological patterns.

Artificial intelligence (AI) is increasingly integrated into modern healthcare, offering powerful support for clinical decision-making. However, in real-world settings, AI systems may experience performance degradation over time, due to factors such as shifting data distributions, changes in patient characteristics, evolving clinical protocols, and variations in data quality. These factors can compromise model reliability, posing safety concerns and increasing the likelihood of inaccurate predictions or adverse outcomes. This review presents a forward-looking perspective on monitoring and maintaining the "health" of AI systems in healthcare. We highlight the urgent need for continuous performance monitoring, early degradation detection, and effective self-correction mechanisms. The paper begins by reviewing common causes of performance degradation at both data and model levels. We then summarize key techniques for detecting data and model drift, followed by an in-depth look at root cause analysis. Correction strategies are further reviewed, ranging from model retraining to test-time adaptation. Our survey spans both traditional machine learning models and state-of-the-art large language models (LLMs), offering insights into their strengths and limitations. Finally, we discuss ongoing technical challenges and propose future research directions. This work aims to guide the development of reliable, robust medical AI systems capable of sustaining safe, long-term deployment in dynamic clinical settings.

Objective: Magnetic Resonance Imaging (MRI)-guided robotic catheter interventions offer improved safety, visualization and control. Catheter actuation with current-carrying microcoils offers fast and responsive actuation without friction and backlash. However, actuation of this type of catheter leads to large void artifacts in MR images. We propose an interleaving method to reduce these artifacts without compromising the catheter's actuation capability.

Methods: Interleaving is accomplished by synchronizing and interleaving catheter actuation and MR image acquisition at a high switching frequency using a TTL trigger from the MR scanner that triggers an Interrupt Service Routine in the catheter firmware. Reduction in catheter duty cycle is compensated using effective currents.

Results: Image artifact analysis showed that there is not a statistically significant difference (at 0.05 significance level) between the artifacts associated with the catheter actuating with the proposed actuation-imaging interleaving and with the catheter at rest. Catheter trajectory tracking experiments showed that the error between pure actuation trajectories and interleaved actuation-imaging trajectories is generally within the error of the tracking algorithm. These results hold for both traditional MR imaging and accelerated imaging suitable for cardiac intervention.

Conclusion: The proposed interleaving scheme effectively removes the artifact generated by current-carrying actuation coils while preserving the actuation capability of the catheter. The method can be used with an accelerated imaging sequence that is relevant for interventional imaging.

Significance: The proposed scheme enables simultaneous/interleaved MR visualization and catheter actuation during intervention.