The motor imagery brain-computer interface (MI-BCI) has the ability to use electroencephalogram (EEG) signals to control and communicate with external devices. By leveraging the unique characteristics of task-related brain signals, this system facilitates enhanced communication with these devices. Such capabilities hold significant potential for advancing rehabilitation and the development of assistive technologies. In recent years, deep learning has received considerable attention in the MI-BCI field due to its powerful feature extraction and classification capabilities. However, two factors significantly impact the performance of deep-learning models. The size of the EEG datasets influences how effectively these models can learn. Similarly, the ability of classification models to extract features directly affects their accuracy in recognizing patterns. In this paper, we propose a Multi-Scale Spatio-Temporal and Dynamic Graph Convolution Fusion Network (MST-DGCN) to address these issues. In the data-preprocessing stage, we employ two strategies, data augmentation and transfer learning, to alleviate the problem of an insufficient data volume in deep learning. By using multi-scale convolution, spatial attention mechanisms, and dynamic graph neural networks, our model effectively extracts discriminative features. The MST-DGCN mainly consists of three parts: the multi-scale spatio-temporal module, which extracts multi-scale information and refines spatial attention; the dynamic graph convolution module, which extracts key connectivity information; and the classification module. We conduct experiments on real EEG datasets and achieve an accuracy of 77.89% and a Kappa value of 0.7052, demonstrating the effectiveness of the MST-DGCN in MI-BCI tasks. Our research provides new ideas and methods for the further development of MI-BCI systems.

Certificateless public key infrastructure (PKI) avoids the key escrow problem associated with identity-based PKI and has recently been widely employed in anonymous communication schemes for vehicular adhoc networks (VANETs). In existing certificateless anonymous signcryption schemes for VANETs, vulnerabilities such as potential attacks involving the substitution of pseudonyms and the forging of pseudonymous public-private key pairs exist due to the lack of “identity-key binding” and “non-linearity processing of public-private key pairs.” To address this issue, we propose an improved certificateless anonymous signcryption scheme based on elliptic curve cryptography. The scheme incorporates bilinear pairing as one of the authentication mechanisms, designs pseudonym generation algorithms and public-private key pair structures, and introduces a pseudonym verification mechanism. The correctness of the scheme is proven under the random oracle model, and its security is extensively demonstrated through detailed discussions on its confidentiality, authentication, unforgeability, anonymity, and traceability. Furthermore, the time and space complexity of the scheme are calculated. By comparing with recently published certificateless signcryption schemes, it is shown that the proposed scheme offers higher security with smaller computational and communication overheads. This certificateless vehicular network signcryption algorithm provides an efficient encryption solution for anonymous communication in vehicular networks, thereby ensuring the rapid development of secure technology for intelligent connected vehicle super terminals.

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.