EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. To address these, we propose an efficient ensemble method called MLCSPTSE-MLP, which aims to reduce the computational cost while achieving superior performance. MLCSP of the ensemble utilizes a Riemannian graph embedding strategy to learn intrinsic low-dimensional sub-manifolds, enhancing discrimination. TSE uses the Euclidean mean as the reference point for tangent space mapping and reducing computational cost. Finally, the ensemble incorporates the MLP classifier to offer improved classification performance. Classification results conducted on three datasets demonstrate that MLCSP-TSE-MLP achieves significant superior performance compared to various competing methods. Notably, the MLCSP-TSE module achieves a remarkable increase in training speed and exhibits much lower test time compared to traditional Riemannian methods. Based on these results, we believe that the proposed MLCSP-TSEMLP is a powerful tool for handling high-dimensional data and holds great potential for practical applications.

Phase synchronization phenomena are directly connected with the underlying neural mechanisms of certain cognitive processes. However, only the amplitude information is utilized in most electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Few of the existing methods can simultaneously measure the amplitude and phase information required for classification. In this study, a novel common amplitude-phase measurement (CAPM) method is proposed. This method is capable of jointly measuring the phase and amplitude information of EEG signals on the Riemannian manifold. The proposed CAPM method comprises a two-step approach. First, a novel Riemannian graph embedding is proposed for dimensionality reduction while performing spatial-spectral filtering. The graph embedding is excellent in capturing the intrinsic features contained by the physiological signal. Second, to enhance robustness, a novel classifier is designed to incorporate the regularized linear regression in the computation of Riemannian distance. Experimental results on two BCI competition datasets demonstrate CAPM can yield high classification performance. The proposed CAPM method is a promising tool in analyzing EEG amplitude-phase characteristics and exhibits great potential in BCI applications.

Spatio-temporal filtering has been widely used for extracting discriminative features in the motor imagery-based brain-computer interface (MI-BCI). In order to obtain high performance, the algorithms need to enhance robustness or find class-discriminative bands for the spatial filter. However, the existing methods either cannot derive the spatial and spectral filters with a unique objective function for guaranteeing convergence or rarely consider the combined optimization of spatial-spectral filters and other patterns for enhancing the discrimination. In this study, we present a novel feature extraction method termed Spectrum-weighted Tensor Discriminant Analysis (SwTDA), which optimizes spectral filters along with spatial filters and other associated patterns by tensor-based discriminant analysis. The proposed method considers intrinsic spatial-spectral-temporal information contained by the physiological signal and hence can identify discriminative characteristics robustly. The effectiveness of the algorithm is demonstrated by comparing it with several state-of-the-art methods on two datasets involving 15 different subjects. Results indicate that the SwTDA method yields higher classification accuracies than the competing methods. Furthermore, interpretable spatial-spectral patterns that are determined by the algorithm can be used for further analysis of the MI-based EEG signal.

This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor–Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces. Closed-form power allocation and maximum weight bipartite matching are integrated to enable efficient user pairing and resource management. Simulation results show that the proposed scheme significantly enhances system performance in terms of throughput, spectral efficiency, and interference management, while enabling robustness against channel uncertainties in dynamic IIoT environments. The findings indicate that combining model-free reinforcement learning with conventional optimization provides a viable solution for adaptive resource management in dynamic UAV-GAP cooperative communication scenarios.

The rapid development of multi-cell integrated sensing and communication (ISAC) systems requires new architectural and algorithmic solutions to tackle two key challenges: severe inter-cell interference that degrades communication quality for edge users, and the difficulty of maintaining reliable sensing performance in dynamic environments. This paper proposes an uncrewed aerial vehicle (UAV)-assisted multi-cell ISAC framework. Therein, the UAV acts as a flying base station to support terrestrial base stations in jointly satisfying users' communication and sensing requirements. To minimize the total transmit power while ensuring communication quality and sensing accuracy, a bi-level deep deterministic policy gradient-based joint resource optimization (DDPG-JRO) algorithm is developed. In the outer level, UAV trajectory optimization is formulated as a Markov decision process, where the trajectory decision for each time slot is updated through rewards from the inner level. In the inner level, user scheduling is determined based on the serving channel gain principle, and optimal power allocation is obtained through semidefinite relaxation and Gaussian randomization. The obtained inner-level solution is incorporated into the outer-level reward, thereby coupling power control, scheduling, and trajectory design within a unified learning framework. Numerical results validate the superiority and efficiency of the proposed DDPG-JRO algorithm.

In the field of brain–computer interfaces (BCI), the decoding of motor imagery EEG signals is significantly hindered by individual differences in EEG signals, which limits the generalization ability of decoding models. To address this challenge, this study proposes a mutual information weighted filter bank regularized common spatial pattern (WFBRCSP) algorithm. The algorithm divides the signal into multiple frequency bands, adaptively assigns subject weights based on the mutual information maximization criterion, and optimizes the covariance matrix with a regularization strategy, significantly improving the robustness of feature extraction. The results on the public BCI competition datasets BCICIII IVa and BCICIV IIb exhibit that the WFBRCSP outperforms traditional CSP, RCSP, FBCSP, FBRCSP, and OFBRCSP methods in terms of classification accuracy (87.87% and 85.92%). In addition, through the mutual information-weighted and regularized spatial filtering of data from different subjects, WFBRCSP demonstrates excellent real-time performance in cross-subject scenarios, validating its practical value in brain–computer interface systems. This study provides a new approach to addressing the issues of individual differences and noise interference in EEG signals.