Electroencephalography (EEG) signal acquisition is often affected by artifacts, challenging applications such as brain disease diagnosis and Brain-Computer Interfaces (BCIs). This paper proposes TF-Denoiser, a deep learning model using a joint time-frequency optimisation strategy for artifact removal. The proposed method first employs a position embedding module to process EEG data, enhancing temporal feature representation. Then, the EEG signals are transformed from the time domain to the complex frequency domain via Fourier transform, and the real and imaginary parts are denoised separately. The multi-attention denoising module (MA-denoise) is used to extract both local and global features of EEG signals. Finally, joint optimisation of time-frequency features is performed to improve artifact removal performance. Experimental results demonstrate that TF-Denoiser outperforms the compared methods in terms of correlation coefficient (CC), relative root mean square error (RRMSE), and signal-to-noise ratio (SNR) on electromyography (EMG) and electrooculography (EOG) datasets. It effectively reduces ocular and muscular artifacts and improves EEG denoising robustness and system stability.

With the rapid development of edge computing in the Industrial Internet, data sharing schemes among edge users require reliable cross-domain authentication and key agreement mechanisms to guarantee the security and reliability of inter-device communication. To tackle the deficiencies of existing group key agreement schemes, including dependence on trusted third parties, high computational overhead, and the difficulty of achieving both privacy preservation and attack resistance, this paper presents a cross-domain authenticated key agreement protocol designed for edge computing environments. This protocol supports anonymous identity authentication between cross-domain users, and innovatively constructs a multi-dimensional virtual iterative cyberspace model to generate massive secure keys via the collaborative iteration of multi-user key sequences. The proposed protocol is decentralized, lightweight, and resistant to replay attacks and man-in-the-middle attacks, while satisfying forward and backward secrecy. Security analysis and performance comparison experiments illustrate that the protocol significantly reduces computational and communication overhead, matches the resource-constrained characteristics of edge devices, and can be widely deployed in large-scale data encryption and sharing scenarios under edge computing environments.

The unilateral limb motor imagery paradigm can effectively address the cognitive dissociation problem among multiple limbs and provide strong technical support for extending the functionality of external devices. However, feature mining and accurate decoding of unilateral limb movements remain challenging. In this study, we propose a feature mining method that combines automatic frequency band boundary localization with regularized common spatial pattern (AFBBL-RCSP), and employ a pinball-loss-based twin support vector machine (Pin-UTSVM) to decode EEG signals corresponding to reaching, turning, and grasping movements. First, multiple optimal frequency band boundaries were identified for each subject using AFBBL. Then, regularized spatial features were extracted from each sub-band, and all features were reduced using Fisher’s discriminant analysis. Finally, the Pin-UTSVM classifier was used to categorize the three types of movement data. The results show that, compared with CSP and RCSP feature mining methods using the fixed 8–30 Hz band, the proposed method improves decoding accuracy by 9.52% and 3.89%, respectively. Compared with fixed single-band feature mining methods based on the α band, β band, and α + β band, the proposed method improves accuracy by 5.56%, 3.89%, and 3.73%, respectively. In addition, compared with existing unilateral limb decoding methods based on temporal-spatial features, temporal-frequency features, and temporal-spatial-temporal-frequency fusion CNN features, the proposed method improves decoding accuracy by 34.93%, 34.09%, and 28.11%, respectively. These results suggest that the proposed AFBBL-RCSP method is effective for unilateral limb motor imagery EEG decoding.

Cross-subject emotion recognition using EEG remains challenging due to substantial inter-individual variability. To address this, we propose a Multi-scale Spatio-Temporal Convolution and Multi-order Gated Spatial-Channel Aggregation Network (MCF-SCA). The model leverages multi-scale spatio-temporal convolution to capture rich temporal and spatial features and applies Fast Fourier Transform to transform EEG signals into the frequency domain, enhancing emotion-related representations. A multi-order spatial-channel aggregation module is then introduced, which adaptively integrates features across spatial and channel dimensions through a gating mechanism, enabling dynamic feature weighting and more expressive emotional representations. Experiments on the DEAP dataset show accuracy gains of up to 11–30% for arousal and 12–31% for valence compared with TSception, CNN, LSTM, EEGNet, and MLP. On the DREAMER dataset, improvements reach 5–33% and 3.7–34%, respectively. These results confirm that MCF-SCA achieves superior accuracy and cross-subject adaptability, providing strong support for emotion-based brain–computer interface applications.