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Networked control systems (NCSs) are widely used in practical applications because of their flexibility in deployment. However, due to the dependence on the communication network, NCSs could be vulnerable to malicious cyberattacks. To address this problem, a novel blockchain technology-assisted networked predictive secure control approach is presented for the first time in this article. First, the introduction of blockchain technology brings a significant boost to the inherent resilience of the NCS in an active manner without relying on any prior knowledge of the system or potential attacks. However, blockchain technology would induce time delays unfavorable to the NCS, which could result in the low real-time performance of the control system. Subsequently, a networked Kalman filter-based predictive control is specially designed to compensate for the low real-time property of blockchain technology. A detailed analysis of the security and stability of the closed-loop NCS with the developed networked predictive secure controller is also presented, while sufficient conditions for the closed-loop system to be simultaneously stable and safe in a probabilistic sense are given. Finally, to verify the performance of the proposed approach in terms of practicality, an experimental prototype of a photovoltaic (PV)-based power generation system subjected to random cyber-attacks is built for voltage regulation. [ABSTRACT FROM AUTHOR]

This brief proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noises. The proposed SA-RNSAF algorithm generalizes different algorithms by defining the robust criterion and sparsity-aware penalty. Furthermore, by alternating optimization of the parameters (AOP) of the algorithm, including the step-size and the sparsity penalty weight, we develop the AOP-SA-RNSAF algorithm, which not only exhibits fast convergence but also obtains low steady-state misadjustment for sparse systems. Simulations in various noise scenarios have verified that the proposed AOP-SA-RNSAF algorithm outperforms existing techniques. [ABSTRACT FROM AUTHOR]

A controller that uses sum-of-squares programming is proposed for the attitude control of fixed-wing aircraft. Firstly, the vehicle’s state space form of attitude model is obtained by utilizing general mechanics. Then an online compute controller is designed to achieve the desired attitude tracking performance by solving contraction control metric related to the linearization area near the equilibrium point. Finally, the effectiveness of the control algorithm is demonstrated by a comparative simulation experiment. [ABSTRACT FROM AUTHOR]

This paper studies distributed diffusion adaptation over clustered multi-task networks in the presence of impulsive interferences and Byzantine attacks. We develop a robust resilient diffusion least mean Geman-McClure-estimation (RDLMG) algorithm based on the cost function used by the Geman-McClure estimator, which can reduce the sensitivity to large outliers and make the algorithm robust under impulsive interferences. Moreover, the mean sub-sequence reduced method, in which each node discards the extreme value information of cost contributions received from its neighbors, can make the network resilient against Byzantine attacks. In this regard, the proposed RDLMG algorithm ensures that all normal nodes converge to their ideal states with cooperation among nodes. A statistical analysis of the RDLMG algorithm is also carried out in terms of mean and mean-square performances. Numerical results evaluate the proposed RDLMG algorithm in applications to multi-target localization and multi-task spectrum sensing. [ABSTRACT FROM AUTHOR]

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches. [ABSTRACT FROM AUTHOR]

The security of data propagation is a critical challengefor the widespread use of networked control systems (NCSs), while the conventional strategies based on estimation techniques cannot solve this issue well. Furthermore, the contradiction between the security and the real-time nature of the control method has not been settled yet. The aim of this article is to propose a novel real-time control method empowered by blockchain technologies to resolve the security problems of NCSs under potential cyberattacks. To facilitate the analysis of blockchain-assisted NCSs, a cyber-authentic-physical system (CAPS) framework is proposed. One of the significant features of the CAPS is that the defense of NCSs against attacks is built by introducing a blockchain protocol. Theoretical analysis reveals the relationship among the scale of the peer-to-peer network, the security of the CAPS, and the latency of NCSs, as well as clarifies the impact of the introduction of the blockchain mechanism on the performance of NCSs. With the networked secure control mechanism designed in this article, the security and stability of NCSs can be achieved simultaneously, and their sufficient conditions are also given. Finally, simulations and practical experiments are presented to show the efficacy of the suggested secure control strategy. [ABSTRACT FROM AUTHOR]

We study the multivariate nonparametric change point detection problem, where the data are a sequence of independent $p$ -dimensional random vectors whose distributions are piecewise-constant with Lipschitz densities changing at unknown times, called change points. We quantify the size of the distributional change at any change point with the supremum norm of the difference between the corresponding densities. We are concerned with the localization task of estimating the positions of the change points. In our analysis, we allow for the model parameters to vary with the total number of time points, including the minimal spacing between consecutive change points and the magnitude of the smallest distributional change. We provide information-theoretic lower bounds on both the localization rate and the minimal signal-to-noise ratio required to guarantee consistent localization. We formulate a novel algorithm based on kernel density estimation that nearly achieves the minimax lower bound, save possibly for logarithm factors. We have provided extensive numerical evidence to support our theoretical findings. [ABSTRACT FROM AUTHOR]

In this article, a nonconformal and nonoverlapping domain decomposition method (DDM) based on surface integral equation (SIE) is proposed for the electromagnetic (EM) scattering or radiation of multiscale metallic targets. To reduce the unknown amount of the conventional SIE for multiscale EM simulation, we apply curved triangular elements and higher order hierarchical vector (HOHV) basis functions to the SIE. Next, to increase the flexibility of geometrical modeling and accelerate the convergence of the presented SIE system for electrically large and multiscale metallic targets, a DDM scheme is further developed to employ a discontinuous Galerkin (DG) approach to glue conformal/nonconformal surface grids between adjacent subdomains. In addition, an interior penalty term is introduced to stabilize the DDM solution, and half edge-based HOHV basis functions are introduced to model the current associated with nonconformal surface elements. Meanwhile, the basis expansion and recombination (BER) technique is introduced to significantly accelerate the matrix-filling and improve the efficiency. The flexibility of basis order selection is further enhanced by the hierarchical characteristic of HOHV bases. Finally, several numerical results are provided to demonstrate the accuracy, efficiency, and flexibility of the proposed HO-DG-DDM. [ABSTRACT FROM AUTHOR]

It is common to believe that passengers are more adversely affected by motion sickness than drivers. However, no study has compared passengers and drivers’ neural activities and drivers experiencing motion sickness (MS). Therefore, this study attempts to explore brain dynamics in motion sickness among passengers and drivers. Eighteen volunteers participated in simulating the driving winding road experiment while their subjective motion sickness levels and electroencephalogram (EEG) signals were simultaneously recorded. Independent Component Analysis (ICA) was employed to isolate MS-related independent components (ICs) from EEG. Furthermore, comodulation analysis was applied to decompose spectra of interest ICs, related to MS, to find the specific spectra-related temporally independent modulators (IMs). The results showed that passengers’ alpha band (8-12 Hz) power increased in correlation with the MS level in the parietal, occipital midline and left and right motor areas, and drivers’ alpha band (8-12 Hz) power showed relatively smaller increases than those in the passenger. Further, the results also indicate that the enhanced activation of alpha IMs in the passenger than the driver is due to a higher degree of motion sickness. In conclusion, compared to the driver, the passenger experience more conflicts among multimodal sensory systems and demand neuro-physiological regulation. [ABSTRACT FROM AUTHOR]

The exponential functional link network (EFLN) has been recently investigated and applied to nonlinear filtering. This brief proposes an adaptive EFLN filtering algorithm based on a novel inverse square root (ISR) cost function, called the EFLN-ISR algorithm, whose learning capability is robust under impulsive interference. The steady-state performance of EFLN-ISR is rigorously derived and then confirmed by numerical simulations. Moreover, the validity of the proposed EFLN-ISR algorithm is justified by the actually experimental results with the application to hysteretic nonlinear system identification. [ABSTRACT FROM AUTHOR]

In this brief, an online learning-based adaptive secondary controller with extended state observer (ESO) is proposed for regulating the voltage and allocating the reactive power in an islanded microgrid. With the active disturbance rejection properties of the ESO technique, two delicately designed neural networks via online learning approaches are proposed to compensate for the unknown/uncertain dynamics (UD) in the microgrid and attenuate the impact of external disturbance. Moreover, a vector of switching factors is introduced to unify the voltage recovery of the critical bus and accurate reactive power-sharing into a single framework. It is also worth noting that the devised approach is deployed in a distributed fashion, which enables the microgrid to acquire the plug-and-play capability. Extensive simulations are conducted to verify the effectiveness of the proposed controller for several cases including islanding and sudden load changing. [ABSTRACT FROM AUTHOR]

By developing sophisticated image priors or designing deep(er) architectures, a variety of image super-resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers). Based on the assumption that better component super-resolver should have larger ensemble weight when performing SR reconstruction, we present a maximum a posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Especially, we introduce a reference dataset, which is composed of high-resolution (HR) and low-resolution (LR) image pairs, to measure the SR abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR estimation should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art nondeep learning-based methods, four latest deep learning-based methods, and one ensemble learning-based method, and prove its effectiveness and superiority on some general image datasets and face image datasets. [ABSTRACT FROM AUTHOR]

The dichotomous coordinate descent (DCD) algorithm has been successfully used for significant reduction in the complexity of recursive least squares (RLS) algorithms. In this brief, we generalize the application of the DCD algorithm to RLS adaptive filtering in impulsive noise scenarios and derive a unified update formula. By employing different robust strategies against impulsive noise, we develop novel computationally efficient DCD-based robust recursive algorithms. Furthermore, to equip the proposed algorithms with the ability to track abrupt changes in unknown systems, a simple variable forgetting factor mechanism is also developed. Simulation results for channel identification scenarios in impulsive noise demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Face hallucination is a technique that reconstructs high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of the human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of the image patch. In addition, when they are confronted with misalignment or the small sample size (SSS) problem, the hallucination performance is very poor. To this end, this paper incorporates the contextual information of the image patch and proposes a powerful and efficient context-patch-based face hallucination approach, namely, thresholding locality-constrained representation and reproducing learning (TLcR-RL). Under the context-patch-based framework, we advance a thresholding-based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulate the case that the HR version of the input LR face is present in the training set, it thus iteratively enhances the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. In addition, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL. [ABSTRACT FROM AUTHOR]

This work develops robust diffusion recursive least-squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however, its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios. [ABSTRACT FROM AUTHOR]

Face sketch synthesis is a crucial issue in digital entertainment and law enforcement. It can bridge the considerable texture discrepancy between face photos and sketches. Most of the current face sketch synthesis approaches directly to learn the relationship between the photos and sketches, and it is very difficult for them to generate the individual specific features, which we call rare characteristics. In this paper, we propose a novel face sketch synthesis approach through residual learning. In contrast to traditional approaches, which aim to reconstruct a sketch image directly (i.e., learn the mapping relationship between the photo and sketch), we aim to predict the residual image by learning the mapping relationship between the photo and residual, i.e., the difference between the photo and sketch, given an observed photo. This technique will render optimizing the residual mapping easier than optimizing the original mapping and deriving rare characteristic information. We also introduce a joint dictionary learning algorithm by preserving the local geometry structure of a data space. Through the learned joint dictionary, we transform the face sketch synthesis from an image space to a new and compact space; the new and compact space is spanned by learned dictionary atoms, where the manifold assumption can be further guaranteed. Results show that the proposed method demonstrates an impressive performance in the face sketch synthesis task on three public face sketch datasets and various real-world photos. These results are derived by comparing the proposed method with several state-of-the-art techniques, including certain recently proposed deep learning-based approaches. [ABSTRACT FROM AUTHOR]

The diffusion least-mean ${p}$ -power algorithm is presented for distributed estimation in impulsive noise environments, which aims to minimize the ${p}$ -norm of the error. However, it suffers from slow convergence rate. In this brief, we propose a diffusion normalized least-mean ${p}$ -power algorithm (DNLMP), motivated by the normalized-based algorithms. To further improve the performance of the DNLMP algorithm, a robust DNLMP (RDNLMP) algorithm is developed for distributed estimation. The RDNLMP algorithm considers the error signal in normalization factor, and therefore can diminish the significance of outliers under impulsive noise environments. Moreover, the steady-state analysis of the RDNLMP algorithm is provided. Both performance analysis and numerical simulations are given to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

Person reidentification (re-id), as an important task in video surveillance and forensics applications, has been widely studied. Previous research efforts toward solving the person re-id problem have primarily focused on constructing robust vector description by exploiting appearance’s characteristic, or learning discriminative distance metric by labeled vectors. Based on the cognition and identification process of human, we propose a new pattern, which transforms the feature description from characteristic vector to discrepancy matrix. In particular, in order to well identify a person, it converts the distance metric from vector metric to matrix metric, which consists of the intradiscrepancy projection and interdiscrepancy projection parts. We introduce a consistent term and a discriminative term to form the objective function. To solve it efficiently, we utilize a simple gradient-descent method under the alternating optimization process with respect to the two projections. Experimental results on public datasets demonstrate the effectiveness of the proposed pattern as compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A space-resolved extreme ultraviolet (EUV) spectrometer has been used to observe the spatial distribution and temporal behavior of core impurity emissions at the experimental advanced superconducting tokamak (EAST), evaluating the effects of resonant magnetic perturbations (RMPs) on impurity concentration. The vertical profiles of impurity emissions from the 0 ≤ Z ≤ 450 mm range results in spectra in the wavelength range of 30 Å $\le \lambda \le 500\,{\mathring {\mathrm {A}}}$. Line emissions of impurity carbon, lithium, and iron were observed for C VI (33.7 Å, 2p–1s), Li III (135 Å, 2p–1s), and Fe XXIII (132.9 Å, 1s22s2p–1s22s2), respectively, whereas the unresolved transition array (UTA) of tungsten in the wavelength range of $\lambda = 48$ to 70 Å is monitored to study tungsten behavior with RMP. It is clearly observed that the intensities of C VI, Li III, Fe XXIII, and W-UTA emissions are reduced by the application of RMP fields, during stages of edge-localized modes (ELMs). In addition, the reduction in Fe and W emissions with $n = 1$ and 2 (n: toroidal mode number) RMP is more significant than that of carbon and lithium emissions, i.e., ~50% to 70% for Fe XXIII and W-UTA, and ~30% for C VI and Li III. Considering that the changes in $n_{e}$ and $T_{e}$ cannot account for the impurity emissions mitigation, the EUV measurements may indicate that RMP can significantly and selectively decrease the impurity accumulation in the core region with slight effects on the main plasma. Spatial distributions of impurity emissions are also measured for different impurity species using the EUV spectrometer, confirming the space-resolved diagnostic’s feasibility to study RMP effects on core impurity concentration. [ABSTRACT FROM AUTHOR]

This communication introduces an approach for designing miniaturized single-feed multiband patch antennas. The size reduction is obtained by loading shorting metalized vias on one edge of the radiating patch, while multiband is obtained by etching inverted multiple U-shapes. A dual-band antenna with a large measured frequency ratio of 2.74 is first discussed. Then triple-band, and further quad-band antennas are, respectively, designed. The antennas have sufficient electrical small size when considering the radius of the minimum enclosing sphere to the radiating patch. A radius value of 0.09\lambda 0 is obtained for the quad-band antenna with respect to the lowest operating frequency centered at 3.04 GHz. Simulated and measured results of the antennas’ reflection coefficient and radiation patterns are provided. Good agreement between the simulation results and measurement results is achieved. The antennas’ measured peak gains and efficiencies vary from 1.43 to 3.06 dBi and 42% to 74%, respectively. Compared with other designs, the proposed antennas exhibit multiband performance by using a simple single-feed structure, realize different combinations of highly isolated frequency bands with wide tunability of frequency ratios, and have directional stable radiation patterns that are compact and of electrically small size. Indeed, they have the potential to meet the practical requirements for wireless applications. [ABSTRACT FROM PUBLISHER]

Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain–computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications. [ABSTRACT FROM PUBLISHER]

We investigate the intensity and phase response of optical switches based on a photonic crystal waveguide coupled to a nanocavity. In particular, we compare the performances of switches with traditional Lorentzian transmission spectrum to switches displaying an asymmetric Fano shape, as obtained by incorporating a partially transmitting element in the waveguide. Compared to traditional Lorentzian structures, the Fano structure shows improved switching contrast and speed without adding any extra phase modulation, corresponding to a much lower chirp parameter. Using a simple and ultracompact InP photonic-crystal Fano structure with broken mirror symmetry, we experimentally demonstrate 20-Gb/s all-optical switching with low-energy consumption. [ABSTRACT FROM PUBLISHER]

In this paper, a 10-Gb/s hybrid optical fiber (OF) and free-space optics (FSO) link as part of a bidirectional long-haul OF transmission for application in outdoor environments such as bridges is proposed. A fiber-Bragg-grating (FBG) sensor head is used for monitoring the condition of a bridge, and in the case of the bridge being damaged, the transmission path is changed over from OF to the FSO link to ensure link connectivity. An Erbium-doped fiber amplifier is used to compensate for losses due to the fiber cable and the free-space channel. At a bit error rate (BER) of 10e-9, the power penalty between the OF and FSO paths is < 1 dB, and the power variations for both paths are ±0.07 and ±0.12 dB, respectively. [ABSTRACT FROM PUBLISHER]

This paper proposes a new methodology for minimizing the size of the periodic element and tuning the resonant frequency of a single-layered frequency-selective surface (FSS). Vertical vias are introduced to the FSS design in the methodology. This via-based methodology can be applied to nearly all of the single-layered FSSs presented in the literature, which exhibit much more convenience in practice than the multilayered FSSs. Through the proposed methodology, users can easily vary the resonant frequency of the considered via-inserted FSS (VI-FSS), adding only some additional vias and without having to modify the original element pattern. In addition, the proposed methodology can be applied to minimize the element size or lower its resonant frequency. To illustrate the methodology, the well-known Jerusalem Cross (JC) FSS was first considered. A high-order equivalent circuit was suggested to model the physical mechanism of the VI-FSS with the applied methodology. A set of closed-form equations was also provided to calculate efficiently the circuit inductances and capacitances, especially those caused by the added vias. The VI-FSS prototype was fabricated and examined in a fully anechoic chamber. Favorable agreement among the proposed circuit, full-wave simulation, and measurement was achieved. For demonstrating the capability of the via-based methodology further, it was applied to two advanced applications: 1) two miniaturized-element FSSs and 2) an FSS absorber. Thus, a significant performance improvement in these applications reconfirmed the capability of the proposed methodology. [ABSTRACT FROM PUBLISHER]

Embodied cognition explores the intricate interaction between the brain, body, and the surrounding environment. The advancement of mobile devices, such as immersive interactive computing and wireless electroencephalogram (EEG) devices, has presented new challenges and opportunities for studying embodied cognition. To address how mobile technology within immersive hybrid settings affects embodied cognition, we propose a target detection multitask incorporating mixed body movement interference and an environmental distraction light signal. We aim to investigate human embodied cognition in immersive projector-based augmented reality (IPAR) scenarios using wireless EEG technology. We recruited and engaged fifteen participants in four multitasking conditions: standing without distraction (SND), walking without distraction (WND), standing with distraction (SD), and walking with distraction (WD). We pre-processed the EEG data using Independent Component Analysis (ICA) to isolate brain sources and K-means clustering to categorize Independent Components (ICs). Following that, we conducted time-frequency and correlation analyses to identify neural dynamics changes associated with multitasking. Our findings reveal a decline in behavioral performance during multitasking activities. We also observed decreases in alpha and beta power in the frontal and motor cortex during standing target search tasks, decreases in theta power, and increases in alpha power in the occipital lobe during multitasking. We also noted perturbations in theta band power during distraction tasks. Notably, physical movement induced more significant fluctuations in the frontal and motor cortex than distractions from social environment light signals. Particularly in scenarios involving walking and multitasking, there was a noticeable reduction in beta suppression. Our study underscores the importance of brain-body collaboration in multitasking scenarios, where the simultaneous engagement of the body and brain in complex tasks highlights the dynamic nature of cognitive processes within the framework of embodied cognition. Furthermore, integrating immersive augmented reality technology into embodied cognition research enhances our understanding of the interplay between the body, environment, and cognitive functions, with profound implications for advancing human-computer interaction and elucidating cognitive dynamics in multitasking.

Visual feedback gain is a crucial factor influencing the performance of precision grasping tasks, involving multiple brain regions of the visual motor system during task execution. However, the dynamic changes in brain network during this process remain unclear. The aim of this study is to investigate the impact of changes in visual feedback gain during precision grasping on brain network dynamics. Sixteen participants performed precision grip tasks at 15% of MVC under low (0.1°), medium (1°), and high (3°) visual feedback gain conditions, with simultaneous recording of EEG and right-hand precision grip data during the tasks. Utilizing electroencephalogram (EEG) microstate analysis, multiple parameters (Duration, Occurrence, Coverage, Transition probability(TP)) were extracted to assess changes in brain network dynamics. Precision grip accuracy and stability were evaluated using root mean square error(RMSE) and coefficient of variation(CV) of grip force. Compared to low visual feedback gain, under medium/high gain, the Duration, Occurrence, and Coverage of microstates B and D increase, while those of microstates A and C decrease. The Transition probability from microstates A, C, and D to B all increase. Additionally, RMSE and CV of grip force decrease. Occurrence and Coverage of microstates B and C are negatively correlated with RMSE and CV. These findings suggest that visual feedback gain affects the brain network dynamics during precision grasping; moderate increase in visual feedback gain can enhance the accuracy and stability of grip force, whereby the increased Occurrence and Coverage of microstates B and C contribute to improved performance in precision grasping. Our results play a crucial role in better understanding the impact of visual feedback gain on the motor control of precision grasping.

Precise sustained force control of the fingers is important for achieving flexible hand movements. However, how neuromuscular compartments within a forearm multi-tendon muscle cooperate to achieve constant finger force remains unclear. This study aimed to investigate the coordination strategies across multiple compartments of the extensor digitorum communis (EDC) during index finger sustained constant extension. Nine subjects performed index finger extensions of 15%, 30%, and 45% maximal voluntary contractions, respectively. High-density surface electromyography signals were recorded from the EDC and then analyzed using non-negative matrix decomposition to extract activation patterns and coefficient curves of EDC compartments. The results showed two activation patterns with stable structures during all tasks: one pattern corresponding to the index finger compartment was named master pattern; whereas the other corresponding to other compartments was named auxiliary pattern. Further, the intensity and stability of their coefficient curves were assessed using the root mean square value (RMS) and coefficient of variation (CV). The RMS and CV values of the master pattern increased and decreased with time, respectively, while the corresponding values of the auxiliary pattern were both negatively correlated with the formers. These findings suggested a special coordination strategy across EDC compartments during index finger constant extension, manifesting as two compensations of the auxiliary pattern for the intensity and stability of the master pattern. The proposed method provides new insight into the synergy strategy across multiple compartments within a forearm multi-tendon during sustained isometric contraction of a single finger and a new approach for constant force control of prosthetic hands.