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Electroencephalography (EEG) is extensively employed in emotion recognition and cognitive workload decoding. However, signal characteristics and inter-subject variability pose significant challenges for deep learning models, particularly due to data scarcity and limited generalization. Although data augmentation (DA) is a critical approach to addressing data scarcity, a notable paucity of systematic reviews exists within deep learning frameworks focused exclusively on these two tasks. Through a systematic review of relevant literature, we summarize commonly used public EEG datasets, input representations, and deep learning classifiers. Subsequently, we focus on analyzing the specific applications and effectiveness of seven categories of DA methods in emotion recognition and cognitive workload decoding. The investigation identifies current challenges in this field, explores future research directions, and provides valuable references for researchers seeking to select and apply DA techniques to enhance model performance.

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.

Motor imagery (MI) electroencephalogram (EEG) decoding plays a critical role in brain–computer interfaces but remains challenging due to large inter-subject variability and limited training data. Existing approaches often struggle with few-shot cross-subject adaptation, as they require either extensive fine-tuning or fail to capture individualized neural dynamics. To address this issue, we propose a Task-Conditioned Prompt Learning (TCPL), which integrates a Task-Conditioned Prompt (TCP) module with a hybrid Temporal Convolutional Network (TCN) and Transformer backbone under a meta-learning framework. Specifically, TCP encodes subject-specific variability as prompt tokens, TCN extracts local temporal patterns, Transformer captures global dependencies, and meta-learning enables rapid adaptation with minimal samples. The proposed TCPL model is validated on three widely used public datasets, GigaScience, Physionet, and BCI Competition IV 2a, demonstrating strong generalization and efficient adaptation across unseen subjects. These results highlight the feasibility of TCPL for practical few-shot EEG decoding and its potential to advance the development of personalized brain–computer interface systems.

Emotional singing can affect vocal performance and the audience’s engagement. Chinese universities use traditional training techniques for teaching theoretical and applied knowledge. Self-imagination is the predominant training method for emotional singing. Recently, virtual reality (VR) technologies have been applied in several fields for training purposes. In this empirical comparative study, a VR training task was implemented to elicit emotions from singers and further assist them with improving their emotional singing performance. The VR training method was compared against the traditional self-imagination method. By conducting a two-stage experiment, the two methods were compared in terms of emotions’ elicitation and emotional singing performance. In the first stage, electroencephalographic (EEG) data were collected from the subjects. In the second stage, self-rating reports and third-party teachers’ evaluations were collected. The EEG data were analyzed by adopting the max-relevance and min-redundancy algorithm for feature selection and the support vector machine (SVM) for emotion recognition. Based on the results of EEG emotion classification and subjective scale, VR can better elicit the positive, neutral, and negative emotional states from the singers than not using this technology (i.e., self-imagination). Furthermore, due to the improvement of emotional activation, VR brings the improvement of singing performance. The VR hence appears to be an effective approach that may improve and complement the available vocal music teaching methods.

Discriminative region localization and efficient feature encoding are crucial for fine-grained object recognition. However, existing data augmentation methods struggle to accurately locate discriminative regions in complex backgrounds, small target objects, and limited training data, leading to poor recognition. Fine-grained images exhibit “small inter-class differences,” and while second-order feature encoding enhances discrimination, it often requires dual Convolutional Neural Networks (CNN), increasing training time and complexity. This study proposes a model integrating discriminative region localization and efficient second-order feature encoding. By ranking feature map channels via a fully connected layer, it selects high-importance channels to generate an enhanced map, accurately locating discriminative regions. Cropping and erasing augmentations further refine recognition. To improve efficiency, a novel second-order feature encoding module generates an attention map from the fourth convolutional group of Residual Network 50 layers (ResNet-50) and multiplies it with features from the fifth group, producing second-order features while reducing dimensionality and training time. Experiments on Caltech-University of California, San Diego Birds-200-2011 (CUB-200-2011), Stanford Car, and Fine-Grained Visual Classification of Aircraft (FGVC Aircraft) datasets show state-of-the-art accuracy of 88.9%, 94.7%, and 93.3%, respectively.

Human limb movement imagery, which can be used in limb neural disorders rehabilitation and brain-controlled external devices, has become a significant control paradigm in the domain of brain-computer interface (BCI). Although numerous pioneering studies have been devoted to motor imagery classification based on electroencephalography (EEG) signal, their performance is somewhat limited due to insufficient analysis of key effective frequency bands of EEG signals. In this paper, we propose a model of multiband decomposition and spectral discriminative analysis for motor imagery classification, which is called variational sample-long short term memory (VS-LSTM) network. Specifically, we first use a channel fusion operator to reduce the signal channels of the raw EEG signal. Then, we use the variational mode decomposition (VMD) model to decompose the EEG signal into six band-limited intrinsic mode functions (BIMFs) for further signal noise reduction. In order to select discriminative frequency bands, we calculate the sample entropy (SampEn) value of each frequency band and select the maximum value. Finally, to predict the classification of motor imagery, a LSTM model is used to predict the class of frequency band with the largest SampEn value. An open-access public data is used to evaluated the effectiveness of the proposed model. In the data, 15 subjects performed motor imagery tasks with elbow flexion / extension, forearm supination / pronation and hand open/close of right upper limb. The experiment results show that the average classification result of seven kinds of motor imagery was 76.2%, the average accuracy of motor imagery binary classification is 96.6% (imagery vs. rest), respectively, which outperforms the state-of-the-art deep learning-based models. This framework significantly improves the accuracy of motor imagery by selecting effective frequency bands. This research is very meaningful for BCIs, and it is inspiring for end-to-end learning research.

Real-time performance and accuracy are two most challenging requirements in virtual surgery training. These difficulties limit the promotion of advanced models in virtual surgery, including many geometric and physical models. This paper proposes a physical model of virtual soft tissue, which is a twist model based on the Kriging interpolation and membrane analogy. The proposed model can quickly locate spatial position through Kriging interpolation method and accurately compute the force change on the soft tissue through membrane analogy method. The virtual surgery simulation system is built with a PHANTOM OMNI haptic interaction device to simulate the torsion of virtual stomach and arm, and further verifies the real-time performance and simulation accuracy of the proposed model. The experimental results show that the proposed soft tissue model has high speed and accuracy, realistic deformation, and reliable haptic feedback.

Event-based vision sensors, inspired by biological neural systems, asynchronously capture local pixel-level intensity changes as a sparse event stream containing position, polarity, and timestamp information. These neuromorphic sensors offer significant advantages in dynamic range, latency, and power efficiency. Their working principle inherently addresses traditional camera limitations such as motion blur and redundant background information, making them particularly suitable for dynamic vision tasks. While recent works have proposed increasingly complex event-based architectures, the computational overhead and parameter complexity of these approaches limit their practical deployment. This paper presents a novel spatiotemporal learning framework for event-based object recognition, utilizing a VGG network enhanced with Convolutional Block Attention Module (CBAM). Our approach achieves comparable performance to state-of-the-art ResNet-based methods while reducing parameter count by 2.3% compared to the original VGG model. Specifically, it outperforms ResNet-based methods like MVF-Net, achieving the highest Top-1 accuracy of 76.4% (pretrained) and 71.3% (not pretrained) on CIFAR10-DVS, and 72.4% (not pretrained) on N-Caltech101. These results highlight the robustness of our method when pretrained weights are not used, making it suitable for scenarios where transfer learning is unavailable. Moreover, our approach reduces reliance on data augmentation. Experimental results on standard event-based datasets demonstrate the framework's efficiency and effectiveness for real-world applications.

s Atrial fibrosis, the hallmark of structural remodeling associated with atrial fibrillation (AF), is characterized by abnormal proliferation of atrial fibroblasts and excessive deposition of extracellular matrix. Transforming growth factor-β1 (TGF-β1)/activin receptor-like kinase 5 (ALK5)/Smad2/3/4 pathway has been reported to be involved in the process. Recent studies have implicated both activin A and its specific downstream component activin receptor-like kinase 4 (ALK4) in stimulating fibrosis in non-cardiac organs. We recently reported that ALK4 haplodeficiency attenuated the pressure overload- and myocardial infarction-induced ventricular fibrosis. However, the role of activin A/ALK4 in the pathogenesis of atrial fibrosis and vulnerability to AF remains unknown. Our study provided experimental and clinical evidence for the involvement of activin A and ALK4 in the pathophysiology of atrial fibrosis and AF. Patients with AF had higher activin A and ALK4 expression in atriums as compared to individuals devoid of AF. After angiotensin-II (Ang-II) stimulation which mimicked atrial fibrosis progression, ALK4-deficient mice showed lower expression of ALK4 in atriums, reduced activation of atrial fibroblasts, blunted atrial enlargement and atrial fibrosis, and further reduced AF vulnerability upon right atrial electrophysiological studies as compared to wild-type littermates. Moreover, we found that apart from the well-known TGF-β1/ALK5 pathway, the activation of activin A/ALK4/smad2/3 pathway played an important role in the pathogenesis of Ang-II-mediated atrial fibrosis and inducibility of AF, suggesting that targeting ALK4 might be a potential therapy for atrial fibrosis and AF.

Mung bean (Vigna radiata (L.) R. Wilczek) rotiform disease is caused by Boeremia exigua var. exigua which affects seriously its yields and quality. During its growth, B. exigua can produce toxins but less is known about it. The biological characters of B. exigua were studied. The ideal culture conditions of mycelial growth were pH 5–8, 25 °C, static, and continuous light for 27 days, mannitol can replace sucrose as the most favorable carbon source in the modified Czapek solution, moreover, the addition of inositol or Vitamin B2 was benefit. The ideal culture conditions of crude toxin production were pH 5–7, 25 °C, static, and continuous light for 21 days, the studies did demonstrate that Czapek medium resulted in high levels of toxin formation, while Malt extract and Richard medium resulted in low production levels of toxins, there were no toxins are produced in PD medium at all. Sucrose and glucose can be used as suitable carbon source for the production of toxins, moreover, the addition of inositol or Vitamin C can stimulate the crude toxin production of B. exigua . The crude toxin of B. exigua has good thermal stability, low sensitivity to various wavelengths of light. It is also shown that crude toxins could inhibit the germination and radicle elongation of mung bean, lead to necrotic spots on leaves and have a strong wilt effect on seedlings of mung bean. It also had different degrees of inhibition to other crops such as sorghum, string bean and so on. In order to better control the disease, laboratory toxicities of 21 fungicides were tested. it revealed that Prochlomz has the greatest inhibitory effect, followed by Prochlomz and Carbendazim.