Electroencephalogram (EEG)-based emotion recognition has attracted increasing attention due to the high temporal resolution of EEG signals. However, accurate EEG-based emotion recognition remains challenging because EEG signals inherently exhibit low signal-to-noise ratios, non-stationarity and considerable inter-individual variability. In addition, brain functional connectivity presents distinct band-dependent characteristics, whereas many existing graph-based methods employ shared or static graphs across spectral bands, limiting their ability to characterize band-specific inter-channel interactions. To address these issues, we propose BSGNet, a band-specific graph learning framework for EEG-based emotion recognition. Specifically, instead of assuming a unified connectivity structure, BSGNet learns a set of band-specific channel adjacency matrices to capture functional interactions associated with different spectral bands. The learned graphs are further integrated into a STGCN backbone to jointly capture spatial dependencies among EEG channels and temporal dynamics of emotional responses. Experimental results on two public EEG emotion recognition datasets show that BSGNet achieves improved average performance over representative deep learning and graph-based baselines, supporting the effectiveness of band-specific connectivity modeling for EEG representation learning.

Electroencephalogram (EEG)-based emotion recognition is a key task in affective computing with wide-ranging applications in mental health monitoring, human-computer interaction, and adaptive systems. While deep learning methods have shown promise in modeling spatiotemporal patterns from EEG signals, they often fail to capture the intrinsic geometric structure of EEG electrode layouts, which is critical for decoding brain activity. In this work, we propose EEGGraphormer, a geometry-aware graph transformer that incorporates spatial priors derived from electrode positions directly into the selfattention mechanism. By introducing a head-specific exponential decay bias and integrating a distance-based modulation into the attention computation, the model effectively captures both local and long-range spatial dependencies. We further employ a convolutional mixer as the temporal encoder to extract intra-channel dynamics efficiently. Experiments on two benchmark datasets, DEAP and MAHNOB-HCI, demonstrate that EEGGraphormer consistently outperforms existing CNN-, RNN-, and GCN-based methods in both arousal and valence classification tasks. Ablation and interpretability analyses confirm that the geometry-aware attention enhances spatial feature learning and attends to physiologically meaningful brain regions. These results validate the effectiveness of geometry-informed attention mechanisms in EEG-based emotion recognition.

Navigating unknown underwater environments is a significant challenge for autonomous underwater vehicles (AUVs), especially those with torpedo-like shapes. Lacking a prior map, these vehicles rely on real-time sensor data for perception. Although online motion planning addresses this challenge, many existing methods are primarily tested on more maneuverable robots, such as multicopters and ground vehicles, and do not account for the unique kinematics of torpedo-shaped AUVs, such as limited lateral movement, or the need for 3D motion planning. In this paper, we propose an online motion planning system specifically designed for torpedo-shaped AUVs to navigate 3D underwater terrain without prior environmental knowledge. The system employs a receding horizon planning framework to ensure safe navigation by replanning the trajectory when collisions are detected or the planning horizon is reached. For trajectory generation, a search-based method is used and utilizes a 3D Dubins curve heuristic to guide the generation of an optimal 3D trajectory that adheres to the AUV’s kinematic constraints. To further enhance safety and smoothness, gradient-based optimization is applied to refine the trajectory. Experiments in simulated environments validate the proposed method, demonstrating its ability to generate safe trajectories for AUVs in complex and unknown environments. We release our code as an open-source package1.

Emotion recognition from EEG signals offers significant advantages in affective computing, as EEG more accurately reflects internal emotional states than other modalities, such as facial expressions or peripheral physiological signals. Modeling and capturing subtle affective changes over time is crucial for real-world applications to achieve better human-computer interaction. However, training such models usually requires segment-level emotion labels, which are costly and may not be feasible. Assigning the overall label to all EEG segments within a trial can lead to inaccurate model training and degraded performance, as emotions evolve continuously. This highlights the need for models capable of learning from trial-wise emotion labels while capturing temporal dynamics of emotional responses within each segment because trial-wise post-stimulus labels are more accessible. To this end, we propose EmotionMIL, an end-to-end EEG-based emotion recognition framework that leverages recent advances in deep multiple instance learning (MIL). This framework enables robust emotion recognition from weakly labeled EEG signals and identifies the most prominent emotional responses. EmotionMIL captures the temporal dynamics of emotions using a retentive self-attention mechanism, which adaptively assigns weights to EEG segments based on their relevance in predicting the overall emotion label. A pseudo-bag augmentation strategy is also introduced to enhance the model's generalization ability by generating additional pseudo-bags from the original ones. Evaluated on three benchmark datasets—DEAP, DREAMER, and SEED—EmotionMIL outperforms state-of-the-art non-MIL and MIL models in both subject-dependent and subject-independent tasks, achieving superior accuracy and F1-score. Ablation study further validates the model design, while visualization results demonstrate that EmotionMIL effectively identifies both spatial EEG patterns and temporal emotional dynamics. These findings underscore EmotionMIL's potential for robust, interpretable emotion recognition, paving the way for real-world applications in emotion-aware systems. The code is available athttps://github.com/yuty2009/emotionmil.

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCI EEG datasets ( N=192) and an emotion recognition EEG dataset ( N=45), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.

Whole-slide image (WSI) classification is fundamental to computational pathology, which is challenging in extra-high resolution, expensive manual annotation, data heterogeneity, etc. Multiple instance learning (MIL) provides a promising way towards WSI classification, which nevertheless suffers from the memory bottleneck issue inherently, due to the gigapixel high resolution. To avoid this issue, the overwhelming majority of existing approaches have to decouple the feature encoder and the MIL aggregator in MIL networks, which may largely degrade the performance. Towards this end, this paper presents a Bayesian Collaborative Learning (BCL) framework to address the memory bottleneck issue with WSI classification. Our basic idea is to introduce an auxiliary patch classifier to interact with the target MIL classifier to be learned, so that the feature encoder and the MIL aggregator in the MIL classifier can be learned collaboratively while preventing the memory bottleneck issue. Such a collaborative learning procedure is formulated under a unified Bayesian probabilistic framework and a principled Expectation-Maximization algorithm is developed to infer the optimal model parameters iteratively. As an implementation of the E-step, an effective quality-aware pseudo labeling strategy is also suggested. The proposed BCL is extensively evaluated on three publicly available WSI datasets, i.e., CAMELYON16, TCGA-NSCLC and TCGA-RCC, achieving an AUC of 95.6%, 96.0% and 97.5% respectively, which consistently outperforms all the methods compared. Comprehensive analysis and discussion will also be presented for in-depth understanding of the method. To promote future work, our source code is released at: https://github.com/Zero-We/BCL.

Abstract In motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

Abstract Due to the low contrast, blurred boundary and intensity inhomogeneity of the images, accurate segmentation of breast cancer lesions with dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) still has great challenges. This paper proposed an improved active contour model (ACM) for segmenting breast cancer lesions in DCE‐MRI images. First, based on the extreme learning machine (ELM) method, a robust function is proposed that combines image intensities and time‐domain features to enhance the difference between the lesions and other tissues. Second, an edge‐stop function (ESF) is introduced by combining the image intensity, time‐domain feature, and Hessian shape index to detect the irregular and blurred boundaries. At the boundary of breast cancer lesions, the energy function of ACM is minimized and the evolution of the contour curve completes, so the accurate lesion region of breast cancer can be segmented. The mean Dice similar coefficient (DICE), Jaccard similarity (JC) and Hausdorff distance (HD) of the segmentation of the proposed model in 50 samples are 85.88±6.62%, 75.72±9.68% and 11.62±4.72 mm, respectively. The results segmented by the proposed ACM are more similar to the manual segmentation than the compared models.

Achieving high classification performance in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often entails a large number of channels, which impedes their use in practical applications. Despite the previous efforts, it remains a challenge to determine the optimal subset of channels in a subject-specific manner without heavily compromising the classification performance. In this article, we propose a new method, called spatiotemporal-filtering-based channel selection (STECS), to automatically identify a designated number of discriminative channels by leveraging the spatiotemporal information of the EEG data. In STECS, the channel selection problem is cast under the framework of spatiotemporal filter optimization by incorporating a group sparsity constraints, and a computationally efficient algorithm is developed to solve the optimization problem. The performance of STECS is assessed on three motor imagery EEG datasets. Compared with state-of-the-art spatiotemporal filtering algorithms using full EEG channels, STECS yields comparable classification performance with only half of the channels. Moreover, STECS significantly outperforms the existing channel selection methods. These results suggest that this algorithm holds promise for simplifying BCI setups and facilitating practical utility.

Goal: P300 spellers are among the most popular types of brain-computer interfaces (BCIs) and are extremely useful assistive devices that enable severely disabled patients to communicate. However, P300 speller performances should be further improved to translate laboratory designs into practical applications. We aimed to design a new speller paradigm that could evoke higher event-related potentials (ERPs) than traditional P300 spellers, thus improving the performance of BCI systems. Methods: We proposed a new P300 speller paradigm based on three-dimensional (3-D) stereo visual stimuli. In this paradigm, flashing buttons are presented in 3-D stereo form. We designed two experiments, one that tested a traditional two-dimensional (2-D) speller and another that tested the proposed 3-D speller. Twelve healthy volunteers participated in our experiments. We compared the ERPs elicited by the 2-D speller and the 3-D speller, and we also compared the classification accuracy, information transfer rate (ITR), and user workload between the two paradigms. Results: The 3-D P300 speller elicited higher amplitudes of P300 waveforms than the traditional 2-D P300 speller. The online experimental results showed that the classification accuracy and the ITR were significantly improved with the 3-D P300 speller. We also found that the user workload of the 3-D P300 speller was significantly lower than that of the 2-D P300 speller. Conclusion : The proposed 3-D P300 speller based on stereo visual stimuli outperformed a traditional 2-D P300 speller. This finding indicates that our 3-D paradigm offers a new method that will improve the performance of P300 BCI systems.

Despite rapid advances in the study of brain–computer interfaces (BCIs) in recent decades, two fundamental challenges, namely, improvement of target detection performance and multidimensional control, continue to be major barriers for further development and applications. In this paper, we review the recent progress in multimodal BCIs (also called hybrid BCIs), which may provide potential solutions for addressing these challenges. In particular, improved target detection can be achieved by developing multimodal BCIs that utilize multiple brain patterns, multimodal signals, or multisensory stimuli. Furthermore, multidimensional object control can be accomplished by generating multiple control signals from different brain patterns or signal modalities. Here, we highlight several representative multimodal BCI systems by analyzing their paradigm designs, detection/control methods, and experimental results. To demonstrate their practicality, we report several initial clinical applications of these multimodal BCI systems, including awareness evaluation/detection in patients with disorder of consciousness (DOC). As an evolving research area, the study of multimodal BCIs is increasingly requiring more synergetic efforts from multiple disciplines for the exploration of the underlying brain mechanisms, the design of new effective paradigms and means of neurofeedback, and the expansion of the clinical applications of these systems.

In recent years, various applications which apply the hybrid brain computer interfaces (BCIs) have been studied. In this paper, we present a hybrid BCI system to operate the explorer with P300 and motor imagery, which is mainly composed of a BCI mouse, a BCI speller and an explorer. Through this system, the user can access to his computer and manipulate (open, close, copy, paste, delete) files such as documents, pictures, music, movies and so on. The system has been tested with 5 subjects, and the experimental results show that the explorer can be successfully operated according to subjects' intention with only a small number of mistakes.