Computed tomography and structured light imaging guided orthopedic navigation puncture system: effective reduction of intraoperative image drift and mismatch.

Background: Image-guided surgical navigation systems are widely regarded as the benchmark for computer-assisted surgical robotic platforms, yet a persistent challenge remains in addressing intraoperative image drift and mismatch. It can significantly impact the accuracy and precision of surgical procedures. Therefore, further research and development are necessary to mitigate this issue and enhance the overall performance of these advanced surgical platforms. Objective: The primary objective is to improve the precision of image guided puncture navigation systems by developing a computed tomography (CT) and structured light imaging (SLI) based navigation system. Furthermore, we also aim to quantifying and visualize intraoperative image drift and mismatch in real time and provide feedback to surgeons, ensuring that surgical procedures are executed with accuracy and reliability. Methods: A CT-SLI guided orthopedic navigation puncture system was developed. Polymer bandages are employed to pressurize, plasticize, immobilize and toughen the surface of a specimen for surgical operations. Preoperative CT images of the specimen are acquired, a 3D navigation map is reconstructed and a puncture path planned accordingly. During surgery, an SLI module captures and reconstructs the 3D surfaces of both the specimen and a guiding tube for the puncture needle. The SLI reconstructed 3D surface of the specimen is matched to the CT navigation map via two-step point cloud registrations, while the SLI reconstructed 3D surface of the guiding tube is fitted by a cylindrical model, which is in turn aligned with the planned puncture path. The proposed system has been tested and evaluated using 20 formalin-soaked lower limb cadaver specimens preserved at a local hospital. Results: The proposed method achieved image registration RMS errors of 0.576 ± 0.146 mm and 0.407 ± 0.234 mm between preoperative CT and intraoperative SLI surface models and between preoperative and postoperative CT surface models. In addition, preoperative and postoperative specimen surface and skeletal drifts were 0.033 ± 0.272 mm and 0.235 ± 0.197 mm respectively. Conclusion: The results indicate that the proposed method is effective in reducing intraoperative image drift and mismatch. The system also visualizes intraoperative image drift and mismatch, and provides real time visual feedback to surgeons.

The effect of rhythmic stimuli with spatial information on sensorimotor synchronization: an EEG and EMG study.

Introduction: Sensorimotor synchronization (SMS) is the human ability to align body movement rhythms with external rhythmic stimuli. While the effects of rhythmic stimuli containing only temporal information on SMS have been extensively studied, less is known about how spatial information affects SMS performance. This study investigates the neural mechanisms underlying SMS with rhythmic stimuli that include both temporal and spatial information, providing insights into the influence of these factors across different sensory modalities. Methods: This study compared the effects temporal information and spatial information on SMS performance across different stimuli conditions. We simultaneously recorded the electroencephalogram (EEG), the electromyogram (EMG), and behavioral data as subjects performed synchronized tapping to rhythmic stimuli. The study analyzed SMS performance under conditions including auditory, visual, and auditory-visual motion stimuli (containing both temporal and spatial information), as well as auditory, visual, and auditory-visual non-motion stimuli (containing only temporal information). Specifically, the research examined behavioral data (i.e., mean asynchrony, absolute asynchrony, and variability), neural oscillations, cortico-muscular coherence (CMC), and brain connectivity. Results: The results demonstrated that SMS performance was superior with rhythmic stimuli containing both temporal and spatial information compared to stimuli with only temporal information. Moreover, sensory-motor neural entrainment was stronger during SMS with rhythmic stimuli containing spatial information within the same sensory modality. SMS with both types of rhythmic stimuli was found to be dynamically modulated by neural oscillations and cortical-muscular coupling in the beta band (13-30 Hz). Discussion: These findings provide deeper insights into the combined effects of temporal and spatial information, as well as sensory modality, on SMS performance. The study highlights the dynamic modulation of SMS by neural oscillations and CMC, particularly in the beta band, offering valuable contributions to understanding the neural basis of sensorimotor synchronization.

DBAII-Net with multiscale feature aggregation and cross-modal attention for enhancing infant brain injury classification in MRI.

Objectives.Magnetic resonance imaging (MRI) is pivotal in diagnosing brain injuries in infants. However, the dynamic development of the brain introduces variability in infant MRI characteristics, posing challenges for MRI-based classification in this population. Furthermore, manual data selection in large-scale studies is labor-intensive, and existing algorithms often underperform with thick-slice MRI data. To enhance research efficiency and classification accuracy in large datasets, we propose an advanced classification model.Approach.We introduce the Dual-Branch Attention Information Interactive Neural Network (DBAII-Net), a cutting-edge model inspired by radiologists' use of multiple MRI sequences. DBAII-Net features two innovative modules: (1) the convolutional enhancement module (CEM), which leverages advanced convolutional techniques to aggregate multi-scale features, significantly enhancing information representation; and (2) the cross-modal attention module (CMAM), which employs state-of-the-art attention mechanisms to fuse data across branches, dramatically improving positional and channel feature extraction. Performances (accuracy, sensitivity, specificity, area under the curve (AUC), etc) of DBAII-Net were compared with eight benchmark models for brain MRI classification in infants aged 6 months to 2 years.Main results.Utilizing a self-constructed dataset of 240 thick-slice brain MRI scans (122 with brain injuries, 118 without), DBAII-Net demonstrated superior performance. On a test set of approximately 50 cases, DBAII-Net achieved average performance metrics of 92.53% accuracy, 90.20% sensitivity, 94.93% specificity, and an AUC of 0.9603. Ablation studies confirmed the effectiveness of CEM and CMAM, with CMAM significantly boosting classification metrics.Significance.DBAII-Net with CEM and CMAM outperforms existing benchmarks in enhancing the precision of brain MRI classification in infants, significantly reducing manual effort in infant brain research. Our code is available athttps://github.com/jiazhen4585/DBAII-Net.

Coixol mitigates Toxoplasma gondii infection-induced liver injury by inhibiting the Toxoplasma gondii HSP70/TLR4/NF-κB signaling pathway in hepatic macrophages.

ETHNOPHARMACOLOGICAL RELEVANCE: Coix seed, the dry mature seed kernel of the gramineous plant coix (Coix lacryma-jobi L. var. ma-yuen Stapf), is widely consumed as a traditional Chinese medicine and functional food in China and South Korea. We have previously demonstrated the protective effect of coixol, a polyphenolic compound extracted from coix, against Toxoplasma gondii (T. gondii) infection-induced lung injury. However, the protective effect of coixol on hepatic injury induced by T. gondii infection have not yet been elucidated. AIM OF THE STUDY: This study explores the impact of coixol on T. gondii infection-induced liver injury and elucidates the underlying molecular mechanisms. MATERIALS AND METHODS: Female BALB/c mice and Kupffer cells (KCs) were employed to establish an acute T. gondii infection model in vivo and an inflammation model in vitro. The study examined coixol's influence on the T. gondii-derived heat shock protein 70 (T.g.HSP70)/toll-like receptor 4 (TLR4)/nuclear factor (NF)-κB signaling pathway in T. gondii-infected liver macrophages. Furthermore, a co-culture system of KCs and NCTC-1469 hepatocytes was developed to observe the impact of liver macrophages infected with T. gondii on hepatocyte injury. RESULTS: Coixol notably inhibited the proliferation of tachyzoites and the expression of T.g.HSP70 in mouse liver and KCs, and attenuated pathological liver injury. Moreover, coixol decreased the production of high mobility group box 1, tumor necrosis factor-α, and inducible nitric oxide synthase by suppressing the TLR4/NF-κB signaling pathway in vitro and in vivo. Coixol also mitigated KCs-mediated hepatocyte injury. CONCLUSIONS: Coixol protects against liver injury caused by T. gondii infection, potentially by diminishing hepatocyte injury through the suppression of the inflammatory cascade mediated by the T.g.HSP70/TLR4/NF-κB signaling pathway in KCs. These findings offer new perspectives for developing coixol as a lead compound for anti-T. gondii drugs.

Comparing developed and emerging nations' Economic development with environmental footprint for low-carbon competitiveness.

This study delves into the intricate relationship between economic growth and its ecological repercussions, employing a comprehensive assessment of ecological footprint across 131 nations. The time period considered for the research spans from 2009 to 2019. Utilizing the CS-ARDL methodology, the results indicate a correlation between reducing ecological footprint and bolstering private sector domestic credit. Additionally, a relationship between diminishing private sector domestic credit of banks and augmenting private sector domestic credit within the financial sector has been identified. In conjunction with other indicators of financial advancement, the significance of domestic lending to the private sector has been underscored. The study reveals a notable reduction in human population's adverse impact on the environment. However, increased levels of energy consumption, foreign direct investment and per capita GDP are associated with an improvement in global quality of life. Particularly noteworthy is the validation of the "pollution haven hypothesis" in the global economic context. The implications of this research are substantial; suggesting that global economic dynamics may support efforts towards environmental conservation. However, outcomes may vary across regions or countries, particularly regarding the emphasis placed by the financial sector on environmental preservation. This study comprehensively examines the complex nexus between economic progress and its ecological consequences, keeping in consideration factors such as financial growth, urbanization, energy consumption and Foreign Direct Investment (FDI).

SSVEP-based visual acuity threshold estimation: linear extrapolation to noise level baseline with various noise definition criteria.

This study aimed to explore the effect of various noise definition criteria in linear extrapolation technique to noise level baseline on steady-state visual evoked potential (SSVEP)-based visual acuity assessment. Four noise definition criteria on frequency-domain, i.e., the mean amplitude at the two adjacent bins of the target frequency, the mean amplitude of a narrow frequency band on either side of the target frequency, the mean amplitude at a broad frequency band except for the target frequency and its harmonic frequencies, and the mean amplitude at a broad frequency band at resting state, corresponding to noise 1, noise 2, noise 3, and noise 4, were introduced to calculate noise level baselines. Then, two experiments were implemented. In experiment 1, electroencephalography (EEG) signals of resting state were recorded for fourteen subjects. In experiment 2, the visual stimuli of vertical sinusoidal gratings at six spatial frequency steps were used to induce SSVEPs for twelve subjects. Finally, SSVEP visual acuity was obtained via the SSVEP visual acuity threshold estimation of linear extrapolation technique to noise level baseline with various noise definition criteria. The bland-Altman analysis found that the difference between subjective Freiburg Visual Acuity and Contrast Test (FrACT) and objective SSVEP visual acuity was - 0.0892, - 0.1071, - 0.0745, and - 0.0804 logMAR and the 95% limit of agreement was 0.2150, 0.2146, 0.2046, and 0.2189 logMAR for noise 1, noise 2, noise 3, and noise 4, respectively, indicating that visual acuity of noise 3 definition criterion, i.e., the mean amplitude at a broad frequency band except for the target frequency and its harmonic frequencies, showed the best performance. This study recommended noise definition criterion 3 of the mean amplitude at a broad frequency band to calculate the noise level baseline in the linear extrapolation of SSVEP-based visual acuity assessment.

Squamous cell carcinoma arising from mature cystic teratoma of the ovary: A case report.

Mature cystic teratoma of the ovary (MCTO) is a type of common disease in clinic. However, the malignant transformation of MCTO occurred rarely, with many unexplained questions. To the best of our knowledge, owing to the rarity of squamous cell carcinoma (SCC) in MCTO, even though there are some previous research works concerning about this rare disease, further exploration and discussion is still necessary to reveal unknown aspects. We present a case of pathologically confirmed SCC in MCTO in a 54-year-old female patient, who underwent surgery and subsequent adjuvant chemotherapy. After the treatment, she recovered well and was followed up until now. SCC in MCTO occurred rarely; clinicians should abandon habitual concepts and make targeted management individually.

Fractional-order state space reconstruction: a new frontier in multivariate complex time series.

This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks.

Intelligent ankle-foot prosthesis based on human structure and motion bionics.

The ankle-foot prosthesis aims to compensate for the missing motor functions by fitting the motion characteristics of the human ankle, which contributes to enabling the lower-limb amputees to take care of themselves and improve mobility in daily life. To address the problems of poor bionic motion of the ankle-foot prosthesis and the lack of natural interaction among the patient, prosthesis, and the environment, we developed a complex reverse-rolling conjugate joint based on the human ankle-foot structure and motion characteristics, the rolling joint was used to simulate the rolling-sliding characteristics of the knee joint. Meanwhile, we established a segmental dynamics model of the prosthesis in the stance phase, and the prosthetic structure parameters were obtained with the optimal prosthetic structure dimensions and driving force. In addition, a carbon fiber energy-storage foot was designed based on the human foot profile, and the dynamic response of its elastic strain energy at different thicknesses was simulated and analyzed. Finally, we integrated a bionic ankle-foot prosthesis and experiments were conducted to verify the bionic nature of the prosthetic joint motion and the energy-storage characteristics of the carbon fiber prosthetic foot. The proposed ankle-foot prosthesis provides ambulation support to assist amputees in returning to social life normally and has the potential to help improve clinical viability to reduce medical rehabilitation costs.

Laparoscopic Sigmoid Vaginoplasty for the Treatment of Mayer-Rokitansky-Kuster-Hauser Syndrome in a Single Center: 20 years' Experience.

INTRODUCTION AND HYPOTHESIS: We investigate the feasibility, safety, and clinical therapeutic effect of laparoscopic sigmoid vaginoplasty in women with Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome. METHODS: We performed a retrospective case review cohort study of 56 patients with MRKHs undergoing laparoscopic sigmoid vaginoplasty in Wuhan Union Hospital between 2000 and 2020, and all patients were followed up. RESULTS: The median operating time was 165 min (120-420 min). The median hospital stay was 10 days (rang 7-15 days). A functional neovagina was created 11-15 cm in length and two fingers in breadth in all patients. No introitus stenosis was observed. No intra- or post-operative complications occurred. Two patients were lost to follow-up after 3 months of outpatient visits. Six patients had no intercourse and were required to wear a vaginal mold occasionally. None of the patients had complained of local irritation or dyspareunia. Patients who had post-surgery sexual intercourse were satisfied with their sexual life and the mean total Female Sexual Function Index (FSFI) score was 25.17 ± 0.63. The cosmetic results were excellent. CONCLUSIONS: The laparoscopic sigmoid vaginoplasty can achieve the goal of making a functional neovagina. The main advantage of this surgical technique is that it is minimally invasive and that there are fewer complications post-operation. It is an acceptable procedure for patients with MRKH syndrome.

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges.

Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination ( [Formula: see text] and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

Towards humanlike grasp in robotic hands: mechanical implementation of force synergies.

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

Application of 18ß-glycyrrhetinic acid in the structural modification of natural products: a review.

18ß-Glycyrrhetinic acid (GA) is an oleane-type pentacyclic triterpene saponin obtained from glycyrrhizic acid by removing 2 glucuronic acid groups. GA and its analogues are active substances of glycyrrhiza aicd, with similar structure and important pharmacological effects such as anti-inflammatory, anti-diabetes, anti-tumor and anti-fibrosis. Although GA combined compounds are in the clinical trial stages, its application potential is severely restricted by its low bioavailability, water solubility and membrane permeability. In this article, synthetic methods and structure-activity relationships (SARs) of GA derivatives from 2018 to present are reviewed based on pharmacological activity. It is hoped that this review can provide reference for the future development of potential GA preclinical candidate compounds, and furnish ideas for the development of pentacyclic triterpenoid lead compounds.

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.

The reliable classification of motor unit action potentials (MUAPs) provides the possibility of tracking motor unit (MU) activities. However, the variation of MUAP profiles caused by multiple factors in realistic conditions challenges the accurate classification of MUAPs. The goal of this study was to propose an effective method based on the convolutional neural network (CNN) to classify MUAPs with high levels of variation for MU tracking. MUAP variation was added artificially in the synthetic electromyogram (EMG) signals and was induced by changing the forearm postures in the experimental study. The proposed overlapped-segment-wise EMG decomposition method and the spike-triggered averaging method were combined to obtain the MUAP waveform samples of individual MUs in the experimental study, and the MUAP profile classification performance was tested. Since the ground-truth of MU discharge activities was known for the synthetic EMG, the MU tracking performance was further verified by mimicking the tracking procedure of MU discharge activities and the spike consistency with the true spike trains was tested in the simulation study. The conventional MUAP similarity index (SI)-based method was also performed as comparison. For both the experimental and the synthetic EMG signals, the CNN-based method significantly improved the MUAP tracking performance compared with the conventional SI-based method manifested as a higher classification accuracy (93.3%±5.4% vs 56.2%±13.9%) in the experimental study or higher spike consistency (71.1%±10.2% vs 29.2%±11.0%) in the simulation study with a smaller variation. These results demonstrated the efficiency and robustness of the proposed method to distinguish MUAPs with large variations accurately. Further development of the proposed method can promote the study on the physiological and pathological changes of the neuromuscular system where tracking MU activities is needed.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection.

OBJECTIVE: The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. METHODS: To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. RESULTS: Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. CONCLUSION: This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SIGNIFICANCE: The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

A quantization algorithm of visual fatigue based on underdamped second order stochastic resonance for steady state visual evoked potentials.

Introduction: In recent years, more and more attention has been paid to the visual fatigue caused by steady state visual evoked potential (SSVEP) paradigm. It is well known that the large-scale application of brain-computer interface is closely related to SSVEP, and the fatigue caused by SSVEP paradigm leads to the reduction of application effect. At present, the mainstream method of objectively quantifying visual fatigue in SSVEP paradigm is based on traditional canonical correlation analysis (CCA). Methods: In this paper, we propose a new SSVEP paradigm visual fatigue quantification algorithm based on underdamped second-order stochastic resonance (USSR) to accurately quantify visual fatigue caused by SSVEP paradigm in different working modes using single-channel electroencephalogram (EEG) signals. This scheme uses the fixed-step energy parameter optimization algorithm we designed, combined with the USSR model, to significantly improve the signal-to-noise ratio of the processed signal at the target characteristic frequency. We not only compared the new algorithm with CCA, but also with the traditional subjective quantitative visual fatigue gold standard Likert fatigue scale. Results: There was no significant difference (p = 0.090) between the quantitative value of paradigm fatigue obtained by the single channel SSVEP processed by the new algorithm and the gold standard of subjective fatigue quantification, while there was a significant difference (p < 0.001***) between the quantitative value of paradigm fatigue obtained by the traditional multi-channel CCA algorithm and the gold standard of subjective fatigue quantification. Discussion: The conclusion shows that the quantization value obtained by the new algorithm can better match the subjective gold standard score, which also shows that the new algorithm is more reliable, which reflects the superiority of the new algorithm.

Bionic intelligent ankle-foot prosthesis based on the conjugate curved surface.

Given the poor biomimetic motion of traditional ankle-foot prostheses, it is of great significance to develop an intelligent prosthesis that can realize the biomimetic mechanism of human feet and ankles. To this end, we presented a bionic intelligent ankle-foot prosthesis based on the complex conjugate curved surface. The proposed prosthesis is mainly composed of the rolling conjugated joints with a bionic design and the carbon fiber energy-storage foot. We investigated the flexibility of the prosthetic ankle joint movement, and the ability of the prosthetic foot to absorb ground impact during the gait cycle. Experimental results showed the matching of the ankle/toe position relationship of the human foot during simulated walking, which is helpful to realize the biomimetic motion of the human foot and ankle. It can also help therapists and clinicians provide better rehabilitation for lower-limb amputees.

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study.

Objective.The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control.Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure.Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R2= 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R2= 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability.Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.

Objective: Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR). Methods: To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features. Results: In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components. Conclusion: This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness. Significance: This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

The effect of motion frequency and sound source frequency on steady-state auditory motion evoked potential.

The ability of humans to perceive motion sound sources is important for accurate response to the living environment. Periodic motion sound sources can elicit steady-state motion auditory evoked potential (SSMAEP). The purpose of this study was to investigate the effects of different motion frequencies and different frequencies of sound source on SSMAEP. The stimulation paradigms for simulating periodic motion of sound sources were designed utilizing head-related transfer function (HRTF) techniques in this study. The motion frequencies of the paradigm are set respectively to 1-10 Hz, 15 Hz, 20 Hz, 30 Hz, 40 Hz, 60 Hz, and 80 Hz. In addition, the frequencies of sound source of the paradigms were set to 500 Hz, 1000 Hz, 2000 Hz, 3000 Hz, and 4000 Hz at motion frequencies of 6 Hz and 40 Hz. Fourteen subjects with normal hearing were recruited for the study. SSMAEP was elicited by 500 Hz pure tone at motion frequencies of 1-10 Hz, 15 Hz, 20 Hz, 30 Hz, 40 Hz, 60 Hz, and 80 Hz. SSMAEP was strongest at motion frequencies of 6 Hz. Moreover, at 6 Hz motion frequency, the SSMAEP amplitude was largest at the tone frequency of 500 Hz and smallest at 4000 Hz. Whilst SSMAEP elicited by 4000 Hz pure tone was significantly the strongest at motion frequency of 40 Hz. SSMAEP can be elicited by periodic motion sound sources at motion frequencies up to 80 Hz. SSMAEP also has a strong response at lower frequency. Low-frequency pure tones are beneficial to enhance SSMAEP at low-frequency sound source motion, whilst high-frequency pure tones help to enhance SSMAEP at high-frequency sound source motion. The study provides new insight into the brain's perception of rhythmic auditory motion.