Electroencephalography (EEG) emotion recognition is critical in human-computer interface applications. EEG is a prevalent tool for discerning various emotional states. However, EEG is confronted with the challenges, including nonstationarity, small amplitude, low signal-to-noise ratio, and significant inter-subject variability. Furthermore, existing methods primarily focus on frequency domain features of EEG signal, overlooking the interchannel relationships. To solve these problems, we introduce a novel approach for emotion recognition using a graph-based conditional distribution-aligned domain adaptation (DA) network. This approach harmonizes both domain-invariant and domain-specific features. With the premise that diverse EEG data share consistent underlying features, we employ dynamic graph convolution to extract the domain-invariant features. Subsequently, we build separate branches for distinct source domains to harness domain-specific features, execute conditional distribution alignment, and eventually determine the scores based on distribution distance. Specifically, the contribution of each source domain is weighted according to its distribution similarity with the target domain, thereby assigning greater importance to more relevant sources. The final outcome is obtained via these weighted scores. The final outcome is ascertained via the associated weighted scores. Based on the leave-one-out cross-validation, we tested our model on the open-source SEED and SEED-IV datasets. The mean accuracy rates were 90.33% and 62.83% in the cross-subject situation, as well as 93.26% and 67.06% in the cross-session situation. Compared with multiple state-of-the-art DA algorithms, our proposed algorithm garners better classification results.

Motor imagery (MI)-based electroencephalography (EEG) stands as a prominent paradigm in the brain–computer interface (BCI) field, which is frequently applied in neural rehabilitation and gaming due to its accessibility and reliability. Despite extensive research dedicated to MI EEG classification algorithms, a notable deficiency still remains: their performance is often optimal only in subject-specific or dataset-specific scenarios, which undermines their generalization capability, hence restricting BCI systems' practical utility in real-world contexts. To address this limitation, this study introduces a cutting-edge approach: a discriminative adversarial network based on spatial–temporal–graph fusion (STG-DAN). This innovation aims to learn features that are not only class-discriminative but also domain-invariant. Specifically, the feature extraction module guarantees the feature discriminativeness by amalgamating spatial–temporal and graph-related features, while the domain alignment module focuses on both global domain and local subdomain. The two modules are incorporated into one adversarial learning framework to facilitate the acquisition of domain-invariant features. Evaluations on two publicly accessible datasets, BCI competition IV 2a and OpenBMI, affirm the superiority of our proposed model (averaged accuracy = 62.94% and 73.01% for the two datasets in cross-subject circumstance, respectively). In cross-dataset circumstances, it also outperforms several state-of-the-art algorithms, attesting to the potency of STG-DAN.

The performance of modern U-shaped neural networks for medical image segmentation has been significantly enhanced by incorporating Transformer layers. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to their high complexity. To address this challenge, we proposed a new lightweight U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It utilized an asymmetrical structural design and incorporated both local and global modules to enhance its capacity for local and global modeling. Additionally, a multi-layer cascade fusion decoding network was designed to further bolster the network's information fusion capabilities. Validation performed on open-source CT, MRI, and dermatology datasets demonstrated that the proposed model outperformed other state-of-the-art methods in handling local-global information, achieving an improvement of 1.46% in Dice coefficient and 2.98 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a network that combines Convolutional Neural Network and Transformer architectures, it achieves competitive segmentation performance with only 6.93 million parameters and 6.6 gigabytes of floating point operations, without the need for pre-training. In summary, the proposed method demonstrated enhanced performance while retaining a simpler model design compared to other Transformer-based segmentation networks.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

OBJECTIVE: The changes in neural drive to muscles associated with modulation of inter-muscular coordination in the upper extremity have not yet been investigated. Such information could help elucidate the neural mechanisms behind motor skill learning. METHODS: Six young, neurologically healthy participants underwent a six-week training protocol to decouple two synergist elbow flexor muscles as a newly learned motor skill in the isometric force generation in upward and medial directions. Concurrent electroencephalography and surface electromyography from twelve upper extremity muscles were recorded in two conditions (As-Trained & Habitual) across two assessments (Week 0 vs. Week 6). Changes to inter-muscular connectivity (IMC), functional muscle networks, cortico-muscular connectivity (CMC), cortico-cortical connectivity (CCC) as well as functional brain network controllability (FBNC) associated with the modulation of inter-muscular coordination patterns were assessed to provide a perspective on the neural mechanisms for the newly learned motor skills. RESULTS: Significant decreases in elbow flexor IMC, CMC, and increases in CCC were observed. No significant changes were observed for FBNC. CONCLUSION: The results of this study suggest that modulating the inter-muscular coordination of the elbow flexor muscle synergy during isometric force generation is associated with multiple yet distinct changes in functional connectivity across the central and peripheral perspectives. SIGNIFICANCE: Understanding the neural mechanisms of modulating inter-muscular coordination patterns can help inform motor rehabilitation regimens.

As the most important organs of sense, human eyes perceive 80% information from our surroundings. Eyeball movement is closely related to our brain health condition. Eyeball movement and eye blink are also widely used as an efficient human-computer interaction scheme for paralyzed individuals to communicate with others. Traditional methods mainly use intrusive EOG sensors or cameras to capture eye activity information. In this work, we propose a system named SmartLens to achieve eye activity sensing using zero-power contact lens. To make it happen, we develop dedicated antenna design which can be fitted in an extremely small space and still work efficiently to reach a working distance more than 1 m. To accurately track eye movements in the presence of strong self-interference, we employ another tag to track the user's head movement and cancel it out to support sensing a walking or moving user. Comprehensive experiments demonstrate the effectiveness of the proposed system. At a distance of 1.4 m, the proposed system can achieve an average accuracy of detecting the basic eye movement and blink at 89.63% and 82%, respectively.

In this article, the problem of asynchronous sliding-mode control (SMC) for a class of nonlinear singular Markovian jump systems (SMJSs) with actuator faults and uncertain transition rates (TRs) is investigated. Based on Takagi-Sugeno (T-S) fuzzy models, the nonlinear SMJSs are transformed to a set of local linear SMJSs connected by the so-called IF-THEN rules. The hidden Markov model is employed to demonstrate the nonsynchronization phenomenon of the jump mode between the plant and the designed controller. In combination with SMC and adaptive control techniques, a new asynchronous adaptive SMC scheme is developed, which has the ability to completely compensate for the effects of actuator faults and parameter uncertainties. Sufficient conditions for the stochastic asymptotic admissability of the closed-loop T-S fuzzy SMJSs are derived, and the design scheme for controller gain matrices is presented. The reachability of the sliding surface can be guaranteed by the designed control law. Finally, two examples are provided to illustrate the effectiveness of the proposed new design techniques.

Spasticity is a common post-stroke syndrome that imposes significant adverse impacts on patients and caregivers. This study aims to improve the efficiency of botulinum toxin (BoNT) in managing spasticity, by utilizing a three-dimensional innervation zone imaging (3DIZI) technique based on high-density surface electromyography (HD-sEMG) recordings. Stroke subjects were randomly assigned to two groups: the control group ([Formula: see text]) which received standard ultrasound-guided injections, and the experimental group ([Formula: see text]) which received 3DIZI-guided injections. The amount of BoNT given was consistent for all subjects. The Modified Ashworth Scale (MAS), compound muscle action potential (CMAP) and muscle activation volume (MAV) from bilateral biceps brachii muscles were obtained at the baseline, 3 weeks, and 3 months after injection. Intra-group and inter-group comparisons of MAS, CMAP amplitude and MAV were performed. An overall improvement in MAS of spastic elbow flexors was observed during the 3-week visit ([Formula: see text]), yet no statistically significant difference found with intra-group or inter-group analysis. Compared to the baseline, a significant reduction of CMAP amplitude and MAV were observed in the spastic biceps muscles of both groups at 3-week post-injection, and returned to approximate baseline value at 12-week post injection. A significantly higher reduction was found in CMAP amplitude ([Formula: see text]% versus [Formula: see text]%, [Formula: see text]) and MAV ([Formula: see text]% versus [Formula: see text]%, [Formula: see text]) in the experimental group compared to the control group. The study has demonstrated preliminary evidence that precisely directing BoNT to the innervation zones (IZs) localized by 3DIZI leads to a significantly higher treatment efficiency improvement in spasticity management. Results have also shown the feasibility of developing a personalized BoNT injection technique for the optimization of clinical treatment for post-stroke spasticity using proposed 3DIZI technique.

This article investigates the problem of event-triggered-based sliding mode control (SMC) for a class of Takagi-Sugeno (T-S) fuzzy systems with actuator failures and signal quantization. The classical dynamic uniform quantization strategy is employed to quantize data in both sensor-controller side and controller-actuator side. A new event-triggered adaptive SMC scheme is proposed to stabilize the fault closed-loop systems, where the event-triggered condition is based on quantized state vectors. Reachability of the proposed sliding surface can be ensured by the designed control scheme. Furthermore, the existence of minimal inter-event time and sufficient conditions under which zeno behavior can be avoided is analyzed and presented. Finally, two examples are provided to demonstrate the effectiveness of the proposed new design techniques.

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a 'top-down' pattern that occurs across four brain network states that arise at different time instants (0-170[Formula: see text]ms, 170-370[Formula: see text]ms, 380-620[Formula: see text]ms, and 620-1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

The ability to perceive and regulate emotion is a key component of cognition that is often disrupted by disease. Current neuroimaging studies regarding emotion regulation have implicated a number of cortical regions and identified several EEG features of interest, including the late positive potential and frontal asymmetry. Unfortunately, currently applied methods generally lack in the resolution necessary to capture focal cortical activity and explore the causal interactions between brain regions. In this paper, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data were simultaneously recorded from 20 subjects undergoing emotion processing and regulation tasks. Cortical activity with high-spatiotemporal resolution and accuracy was reconstructed using a novel multimodal EEG/fMRI integration method. A detailed causal brain network associated with emotion processing and regulation was then identified, and the network changes that facilitate different emotion conditions were investigated. The cortical activity of the ventrolateral prefrontal (VLPFC) and posterior parietal cortices depicted conditionally-sensitive spike and wave patterns evidenced in inter-regional communication. The VLPFC was found to behave as a main network source, with conditionally-specific interactions supporting emotional shifts. The results provide unique insight into the cortical activity that supports emotional perception and regulation, the origins of known EEG phenomena, and the manner in which brain regions coordinate to affect behavior.

Heterogeneity among patients always leads to different progression patterns and may require different types of therapy in clinical diagnosis. Therefore, it is crucial to study patient subgrouping. Normally, patient subgrouping is an unsupervised work due to the lack of labeled data. Analysing patients with complex medical data is challenging because of the data multiformity and time irregularity. To handle these issues, we propose a time-sensitive hybrid learning model to subgroup patients. First, we divide the multiform clinical data into two parts: non-time series data and time series data. Then we utilize basic autoencoder (AE) which is a commonly used unsupervised algorithm to learn patients' representations from non-time series data, and we use a recurrent neural network (RNN) based AE to extract representations from time series data. To capture the time irregularity in time series data, we propose a time-sensitive RNN which utilizes the time intervals to control the decaying degree of history memories. Finally, we present a weighted k-means method to subgroup patients with the pairwise representations. Experiments on real world medical datasets demonstrate that our proposed model can effectively improve the validity of patient subgrouping.

This article proposes a new approach to accomplish the mission of orbital transfer using the two finite variable thrusts. This problem requires attaining the two optimal time histories of the acceleration in the each thrust arc, which can be expressed as a polynomial function of time, and taking the corresponding coefficients as potential solutions can be obtained by using the particle swarm optimization technique. Furthermore, combining with penalty function method to solve the trajectory terminal constraints problems make final states completely free without and with rendezvous problem. Then, the optimal values of all the unknown parameters of the problem under considering the minimum fuel-consumption are obtained. Both the initial and final states of the trajectory are considered with unknown and known. In order to avoid collision with on-orbit spacecraft at the final orbit, the transfer trajectory should not be intersection with the final orbit except the final states. Finally, for the four trajectories optimization problems, the numerical results shown that the minimum-fuel can be obtained by considering the unknown or known coast arc.

In multi-class support vector machine (MSVM) for classification, one core issue is to regularize the coefficient vectors to reduce overfitting. Various regularizers have been proposed such as L2, L1, and trace norm. In this paper, we introduce a new type of regularization approach -- angular regularization, that encourages the coefficient vectors to have larger angles such that class regions can be widen to flexibly accommodate unseen samples. We propose a novel angular regularizer based on the singular values of the coefficient matrix, where the uniformity of singular values reduces the correlation among different classes and drives the angles between coefficient vectors to increase. In generalization error analysis, we show that decreasing this regularizer effectively reduces generalization error bound. On various datasets, we demonstrate the efficacy of the regularizer in reducing overfitting.

There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.

A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multichannel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multistep iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering-Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of -10 dB. More than 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A "two-source" test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multichannel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction.

This paper presents a new algorithm for real time tracking of the exterior and interior surfaces of organs using sparse sampling of the exterior surfaces. The tracking is based on identifying subspaces in which the coefficients of spherical harmonic representations of the surfaces live. It uses pre operative CT/MRI scans during training, and needlescopic images acquired during tracking. We study different strategies for sampling the exterior organ surface using the needlescopic images and also apply the method to 3D frame interpolation. Specially, we provide (1)the first demonstration of real time interior organ surface reconstruction using sparse sampling of exterior surfaces with error rate as low as 0.095%, and (2) algorithms' application in 3D cardiac frame interpolation with error rate of only 1.15%while reducing radiation rate by 90%.