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.

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.

Electroencephalography (EEG) source imaging (ESI) methods aim to reconstruct cortical sources from scalp EEG signals, a crucial task for understanding the normal brain as well as brain disorders. Traditional model-driven ESI methods face challenges in real-time reconstruction, while deep neural network (DNN)-based ESI methods often struggle with generalization to new data. To address these issues, we propose ADMM-ESINet, a novel deep unfolding neural network for robust and efficient reconstruction of EEG extended sources. ADMM-ESINet leverages a structured sparsity constraint within a regularization framework and employs the Alternating Direction Method of Multipliers (ADMM) to achieve iterative solutions. By unrolling the ADMM algorithm into a cascaded network architecture, ADMM-ESINet effectively integrates prior knowledge, enabling end-to-end, real-time ESI. Crucially, both the regularization parameters and the spatial transform operator are learned directly from the training data. Numerical results demonstrate that ADMM-ESINet surpasses traditional DNN-based methods in generalization ability and accurately reconstructs the location, extent, and temporal dynamics of extended sources, establishing ADMM-ESINet as a promising method for real-time ESI.

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and epilepsy EEG dataset demonstrate that the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.

Grant-free non-orthogonal multiple access (NOMA) has recently received wide attention for reducing signaling overhead and transmission latency in massive machine-type communications (mMTC). In grant-free NOMA systems, user activity and data (UAD) has to be detected, which is challenging in practice. As an emerging technique, compressive sensing (CS) shows great promise in solving this problem by exploiting the inherent sparsity nature of user activity. This paper proposes to use the weighted$\ell _{2, 1}$minimization (WL21M) to jointly detect UAD in realistic dynamic scenarios. At first, the average recoverability of the WL21M is analyzed. This analysis reveals the fact that the WL21M can improve the detection performance by means of an appropriate weighting and the incorporation of intrinsic temporal correlation. Motivated by the analysis, a collaborative hierarchical match pursuit (C-HiMP) algorithm is proposed for dynamic UAD detection. In the C-HiMP, a sequence of WL21M problems are solved in the subspaces spanned by all of the components in the hierarchical estimated support sets, where the weights are collaboratively updated by the solutions in previous time slots so that an attractive self-correction capacity is obtained. Simulation results demonstrate that the proposed C-HiMP can obtain significant performance improvements, in terms of detection accuracy and computational complexity, compared with several state-of-the-art CS-based detection algorithms.

In this paper, we address the problem of optimizing the power allocation at each time slot for energy-harvesting sensors in an IoT system, where each sensor transmits its observation via a coherent multiple access channel, and thus the observation vector received at the fusion center (FC) becomes a compressed version of the original observations. Our goal is to minimize the number of transmissions required by the high-fidelity reconstruction of the original observations in the FC. In this scenario, the power allocation, coupling with the channel effect, constitute an effective measurement matrix in compressive sensing. Because its performance decides the requirement on the number of transmissions, the goal can be achieved through constructing the measurement matrix as good as possible, or equivalently, solving the power optimization allocation problem, subject to the available energy constraints at each sensor. However, the sensors can only obtain unreliable and intermittent available energy, so that the traditional performance metric, i.e., mutual coherence (MC), of the measurement matrix cannot be directly used to guide the optimization, because an “equal-norm columns” assumption is implicitly required, but not satisfied in our scenario due to the available energy constraints. Moreover, this optimization problem is also non-convex. To overcome these obstacles, we first carry out a distortion analysis based on the generalized MC, which abandons the “equal-norm columns” assumption. The theoretical results indicate that the measurement matrix construction can be formulated as an optimization problem that not only minimizes the MC, but also minimizes the maximum and maximizes the minimum of the column norms of the effective matrix. We further transform this problem into a sequence of surrogate convex problems and iteratively find the solution. Numerical results show that the proposed framework improves the tradeoffs between reconstruction accuracy and the number of transmissions over various power allocation strategies. In some cases, where other strategies achieve a probability of exact recovery of below 0.7, the proposed framework can achieve a more than 0.9 probability.

Compressive sensing (CS) aims to accurately reconstruct high-dimensional signals from a small number of measurements by exploiting signal sparsity and structural priors. However, signal priors utilized in existing CS reconstruction algorithms rely mainly on hand-crafted design, which often cannot offer the best sparsity-undersampling tradeoff because high-order structural priors of signals are hard to be captured in this manner. In this article, a new recovery guarantee of the unified CS reconstruction model-weighted ℓ1minimization (WL1M) is derived, which indicates universal priors could hardly lead to the optimal selection of the weights. Motivated by the analysis, we propose a deep unfolding network for the general WL1M model. The proposed deep unfolding-based WL1M (D-WL1M) integrates universal priors with learning capability so that all of the parameters, including the crucial weights, can be learned from training data. We demonstrate the proposed D-WL1M outperforms several state-of-the-art CS-based methods and deep learning-based methods by a large margin via the experiments on the Caltech-256 image data set.

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.

It is difficult to control high degree of freedom (DOF) robotic manipulator based on brain computer interface (BCI). This paper proposes a BCI system which utilizes steady-state visual evoked potential (SSVEP) for controlling a 4-DOF robotic manipulator. In order to elicit SSVEP responses, a stimulator panel circumferentially equipped with 12 LEDs as flickering sources is designed to provide visual stimuli. Applying canonical correlation analysis (CCA) method, elicited SSVEP responses can be decoded as movement commands. Furthermore, the novel commands mapping is designed for controlling end effector to move in 3D space in an efficient and accurate manner. Online experiments were carried out and three subjects participated in this study to perform move-grasp tasks in the simulation environment. Seven out of nine tasks were successfully completed within 50 trials with an average time of 156.71 seconds. The results show that it is feasible and efficient to control 4-DOF robotic manipulator with the proposed SSVEP-based BCI.

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.

The problem of robust alignment of batches of images can be formulated as a low-rank matrix optimization problem, relying on the similarity of well-aligned images. Going further, observing that the images to be aligned are sampled from a union of low-rank subspaces, we propose a new method based on subspace recovery techniques to provide more robust and accurate alignment. The proposed method seeks a set of domain transformations which are applied to the unaligned images so that the resulting images are made as similar as possible. The resulting optimization problem can be linearized as a series of convex optimization problems which can be solved by alternative sparsity pursuit techniques. Compared to existing methods like robust alignment by sparse and low-rank models, the proposed method can more effectively solve the batch image alignment problem, and extract more similar structures from the misaligned images.

In this paper, we study the performance guarantee of weighted ℓ1-constrained quadratic programming in recovering the support of a sparse signal from a few linear measurements. A new sufficient condition for the success of weighted ℓ1-constrained quadratic programming is derived. Further, we demonstrate its applications in two typical weighted ℓ1models: Firstly, a theoretical result on the modified-BPDN can be generated directly, which indicates that if a partial support information is available and more than half of the information is accurate, then modified-BPDN relaxes the sufficient condition of BPDN ensuring the support recovery for any sparse signals. Secondly, the proposed condition offers a criterion to compute the optimal weights of weighted ℓ1minimization for nonuniform sparse models. Some simulations are carried out to validate its effectiveness.

For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

P300 is one of the most studied components of event related potentials which reflects the responses of brain to events in the external environment. In this paper, we present a new method that utilizes multiresolution autoregression of multichannel time series (MAMTS) for feature extraction of P300 wave. First, it adopts multiresolution autoregression on dyadic tree to depict the characteristic of electroencephalogram (EEG) signal. Then the corresponding autoregression noise of multichannel time series is extracted as the feature. The experiment results verified the effectiveness of this new feature for P300 speller brain compute interface (BCI).

Many advanced adaptive beamformers are robust against arbitrary array steering vector (ASV) mismatches within a presumed uncertainty set. Adaptive array tolerating significant steering direction error usually requires a large size of ASV uncertainty set. In such case, however, the output signal-to-interference-plus-noise ratios (SINRs) of robust methods degrade quickly with the increasing size of the uncertainty set. In this paper, we propose a new compact ASV uncertainty set which is modelled explicitly by the uncertainty on steering direction and the other arbitrary ASV errors. A robust adaptive beamformer is derived based on this new ASV uncertainty set. To eliminate the non-convex constraint on array magnitude response, we force the real part of array response to exceed unity regarding the ASVs within the uncertainty set. Furthermore, using the worst-case optimization technique, the resultant beamformer is formulated as a quadratic optimization problem with semi-infinite second-order cone (SOC) constraints. Numerical studies show that a large robust response region is easy to achieve and the resultant beamformer achieves high performance on SINR enhancement.

A novel robust adaptive beamformer, with new robust constraints on array magnitude response was proposed by utilizing the autocorrelation sequence of array weight vector and the worst-case optimization technique. The proposed adaptive beamformer was formulated as a linear programming problem with second-order cone semi-infinite constraints, which can be eliminated by using the sampling technique. In this paper, we transform these semi-infinite second-order cone constraints into some norm constraints and linear matrix inequality (LMI) constraints. The advantage of this new formulation of the problem is that the sampling of the angles is avoided. The exact optimal result of the problem can be obtained instead of the approximated one provided by sampling technique. The resultant beamformer possesses superior robustness against arbitrary array imperfections and high performance on signal-to-interference-plus-noise ratio (SINR) enhancement even with a large controlled robust response region.

This letter investigates a transmitted signaling technique using orthogonal unified complex Hadamard transform (UCHT) spreading sequences and the coherent RAKE receiver in direct-sequence code-division multiple-access (DS-CDMA) downlinks to maintain the orthogonality between users and reduce the effect of multipath fading and interference from other users. A general multipath-fading channel model is assumed. System performance is evaluated by means of signal-to-interference-plus-noise ratio (SINR) at the RAKE receiver. It is shown that the SINR of the system employing UCHT complex sequences is independent of the phase offsets between different paths, while the SINR of the system using Walsh-Hadamard (WH) sequences is related to the squared cosine of path phase offsets. As a result, the bit-error ratio performance of the DS-CDMA downlink system employing UCHT complex sequences is better than that of the system with WH sequences at high SINRs

Quadriphase complex sequences based on unified complex Hadamard transform (UCHT) are orthogonal and easy to generate [6]. There are sixty-four sets in UCHT-based sequences. Some sets of UCHT sequences provide better auto-correlation properties than orthogonal walsh-Hadamard (WH) sequences. In this paper, UCHT-based sequences are applied as spreading sequences in an asynchronous MC-DS-CDMA system. The bit-error rate (BER) performance of the underlying system with complex spreading sequences is investigated and simulation results show that the asynchronous MC-DS-CDMA system spread by UCHT sequences outperforms that spread by WH sequences in frequency selective fading channel.

This paper presents an approach based on a preprocessing technique that allows the conventional subspace tracking method to be adapted to the case of coherent or highly correlated multipath signals in fading channels. The preprocessing technique, known as temporal smoothing, makes use of the independent fading property of the signal paths to decorrelate them. It gives better resolution than the common spatial smoothing methods in tracking the directions of the coherent paths.

Subspace-based direction finding techniques, such as MUSIC, encounter great difficulty when the received signals on the antenna array are coherent. This problem can be solved by conventional spatial smoothing methods, which require at least 3M/2 antennas to resolve M coherent signals. A previously proposed preprocessing approach called temporal smoothing requires only M+1 antennas given M coherent signals. In this letter, a block-sampling temporal smoothing scheme is introduced, and we derive the MSE in the bearing estimation with temporal smoothing. Finally, the performance of the temporal smoothing is compared with the spatial smoothing.