生物医学工程学杂志最新文献

Brain-computer interface (BCI) technology faces structural risks due to a misalignment between its technological maturity and industrialization expectations. This study used the Technology Readiness Level (TRL) framework to assess the status of major BCI paradigms-such as steady-state visual evoked potential (SSVEP), motor imagery, and P300-and found that they predominantly remained at TRL4 to TRL6, with few stable applications reaching TRL9. The analysis identified four interrelated sources of bubble risk: overly broad definitions of BCI, excessive focus on decoding performance, asynchronous translational progress, and imprecise terminology usage. These distortions have contributed to the misallocation of research resources and public misunderstanding. To foster the sustainable development of BCI, this paper advocated the establishment of a standardized TRL evaluation system, clearer terminological boundaries, stronger support for fundamental research, enhanced ethical oversight, and the implementation of inclusive and diversified governance mechanisms.

Small animal multimodal biomedical imaging refers to the integration of multiple imaging techniques within the same system or device to acquire comprehensive physiological and pathological information of small animals, such as mice and rats. With the continuous advancement of biomedical research, this cutting-edge technology has attracted extensive attention. Multimodal imaging techniques, based on diverse imaging principles, overcome the limitations of single-modal imaging through information fusion, significantly enhancing the overall system's sensitivity, temporal/spatial resolution, and quantitative accuracy. In the future, the integration of new materials and artificial intelligence will further boost its sensitivity and resolution. Through interdisciplinary innovation, this technology is expected to become the core technology of personalized medicine and expand its applications to drug development, environmental monitoring, and other fields, thus reshaping the landscape of biomedical research and clinical practice. This review summarized the progress on the application and investigation of multimodal biomedical imaging techniques, and discussed its development in the future.

Brain age prediction, as a significant approach for assessing brain health and early diagnosing neurodegenerative diseases, has garnered widespread attention in recent years. Electroencephalogram (EEG), an non-invasive, convenient, and cost-effective neurophysiological signal, offers unique advantages for brain age prediction due to its high temporal resolution and strong correlation with brain functional states. Despite substantial progress in enhancing prediction accuracy and generalizability, challenges remain in data quality and model interpretability. This review comprehensively examined the advancements in EEG-based brain age prediction, detailing key aspects of data preprocessing, feature extraction, model construction, and result evaluation. It also summarized the current applications of machine learning and deep learning methods in this field, analyzed existing issues, and explored future directions to promote the widespread application of EEG-based brain age prediction in both clinical and research settings.

In recent years, hybrid brain-computer interfaces (BCIs) have gained significant attention due to their demonstrated advantages in increasing the number of targets and enhancing robustness of the systems. However, Existing studies usually construct BCI systems using intense auditory stimulation and strong central visual stimulation, which lead to poor user experience and indicate a need for improving system comfort. Studies have proved that the use of peripheral visual stimulation and lower intensity of auditory stimulation can effectively boost the user's comfort. Therefore, this study used high-frequency peripheral visual stimulation and 40-dB weak auditory stimulation to elicit steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR) signals, building a high-comfort hybrid BCI based on weak audio-visual evoked responses. This system coded 40 targets via 20 high-frequency visual stimulation frequencies and two auditory stimulation frequencies, improving the coding efficiency of BCI systems. Results showed that the hybrid system's averaged classification accuracy was (78.00 ± 12.18) %, and the information transfer rate (ITR) could reached 27.47 bits/min. This study offers new ideas for the design of hybrid BCI paradigm based on imperceptible stimulation.

Heart rate variability time and frequency indices are widely used in functional assessment for autonomic nervous system (ANS). However, this method merely analyzes the effect of cardiac dynamics, overlooking the effect of cardio-pulmonary interplays. Given this, the present study proposes a novel cardiopulmonary coupling (CPC) algorithm based on cross-wavelet transform to quantify cardio-pulmonary interactions, and establish an assessment system for ANS aging effects using wearable electrocardiogram (ECG) and respiratory monitoring devices. To validate the superiority of the proposed method under nonstationary and low signal-to-noise ratio conditions, simulations were first conducted to demonstrate the performance strength of the proposed method to the traditional one. Next, the proposed CPC algorithm was applied to analyze cardiac and respiratory data from both elderly and young populations, revealing that young populations exhibited significantly stronger couplings in the high-frequency band compared with their elderly counterparts. Finally, a CPC assessment system was constructed by integrating wearable devices, and additional recordings from both elderly and young populations were collected by using the system, completing the validation and application of the aging effect assessment algorithm and the wearable system. In conclusion, this study may offers methodological and system support for assessing the aging effects on the ANS.

Convolutional neural networks (CNNs) are renowned for their excellent representation learning capabilities and have become a mainstream model for motor imagery based electroencephalogram (MI-EEG) signal classification. However, MI-EEG exhibits strong inter-individual variability, which may lead to a decline in classification performance. To address this issue, this paper proposes a classification model based on dynamic multi-scale CNN and multi-head temporal attention (DMSCMHTA). The model first applies multi-band filtering to the raw MI-EEG signals and inputs the results into the feature extraction module. Then, it uses a dynamic multi-scale CNN to capture temporal features while adjusting attention weights, followed by spatial convolution to extract spatiotemporal feature sequences. Next, the model further optimizes temporal correlations through time dimensionality reduction and a multi-head attention mechanism to generate more discriminative features. Finally, MI classification is completed under the supervision of cross-entropy loss and center loss. Experiments show that the proposed model achieves average accuracies of 80.32% and 90.81% on BCI Competition IV datasets 2a and 2b, respectively. The results indicate that DMSCMHTA can adaptively extract personalized spatiotemporal features and outperforms current mainstream methods.

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.

In order to characterize the characteristics of pathological tremor of human upper limb, a simulation system of pathological tremor of human arm was provided and its dynamic response was analyzed. Firstly, in this study, a two-degree-of-freedom human arm dynamic model was established and linearized according to the arbitrary initial angle of joints. After solving the analytical solutions of steady-state responses of the joints, the numerical solution was used to verify it. The results of theoretical analysis show that the two natural frequencies of the developed dynamic model are 2.9 Hz and 5.4 Hz, respectively, which meet the characteristic frequency range of pathological tremors. Then, combined with the measured parameters of human arm, a tremor simulation system was built, and the measured results of joint responses are in good agreement with the theoretical and simulation analysis results, which verifies the effectiveness of the theoretical model. The results show that the human arm pathological tremor simulation system designed in this paper can characterize the frequency and response amplitude of the human upper limb pathological tremor. Moreover, the relevant research lays a theoretical foundation and experimental conditions for the subsequent development of wearable tremor suppression devices.

In order to solve the problems of difficult test, high cost and long cycle in the development of large-scale airborne negative pressure isolation system, the simulation analysis of negative pressure response characteristics is carried out around various aviation conditions such as aircraft ascending, leveling and descending, especially rapid decompression, based on the computational fluid dynamics (CFD) method. The results showed that the isolation cabin could achieve -50 Pa pressure difference environment and form a certain pressure gradient. The exhaust air volume reached the maximum value in the early stage of the aircraft's ascent, and gradually decreased with the increase of altitude until it was level flying. In the process of aircraft descent, the exhaust fan could theoretically maintain a pressure difference far below -50 Pa without working; Under the special condition of rapid pressure loss, it was difficult to deal with the rapid change of low pressure only by the exhaust fan, so it was necessary to design safety valve and other anti-leakage measures in the isolation cabin structure. Therefore, the initial stage of aircraft ascent is the key stage for the adjustment and control of the negative pressure isolation system. By controlling the exhaust air volume and adjusting parameters, it can adapt to the change of low pressure under normal flight conditions, form a relatively stable negative pressure environment, and meet the needs of biological control, isolation and transport.

The lumbar intervertebral disc exhibits a complex physiological structure with interactions between various segments, and its components are extremely complex. The material properties of different components in the lumbar intervertebral disc, especially the water content (undergoing dynamic change as influenced by age, degeneration, mechanical loading, and proteoglycan content) - critically determine its mechanical properties. When the lumbar intervertebral disc is under continuous pressure, water seeps out, and after the pressure is removed, water re-infiltrates. This dynamic fluid exchange process directly affects the mechanical properties of the lumbar intervertebral disc, while previous isotropic modeling methods have been unable to accurately reflect such solid-liquid phase behaviors. To explore the load-bearing mechanism of the lumbar intervertebral disc and establish a more realistic mechanical model of the lumbar intervertebral disc, this study developed a solid-liquid biphasic, fiber-reinforced finite element model. This model was used to simulate the four movements of the human lumbar spine in daily life, namely flexion, extension, axial rotation, and lateral bending. The fluid pressure, effective solid stress, and liquid pressure-bearing ratio of the annulus fibrosus and nucleus pulposus of different lumbar intervertebral discs were compared and analyzed under the movements. Under all the movements, the fluid pressure distribution was closer to the nucleus pulposus, while the effective solid stress distribution was more concentrated in the outer annulus fibrosus. In terms of fluid pressure, the maximum fluid pressure of the lumbar intervertebral disc during lateral bending was 1.95 MPa, significantly higher than the maximum fluid pressure under other movements. Meanwhile, the maximum effective solid stress of the lumbar intervertebral disc during flexion was 2.43 MPa, markedly higher than the maximum effective solid stress under other movements. Overall, the liquid pressure-bearing ratio under axial rotation was smaller than that under other movements. Based on the solid-liquid biphasic modeling method, this study more accurately revealed the dominant role of the liquid phase in the daily load-bearing process of the lumbar intervertebral disc and the solid-phase mechanical mechanism of the annulus fibrosus load-bearing, and more effectively predicted the solid-liquid phase co-load-bearing mechanism of the lumbar intervertebral disc in daily life.