Li Yang

Wenchao Yang, Weidong Yan, Wenkang Liu et al.

Large-scale pre-trained models hold significant potential for learning universal EEG representations. However, most existing methods, particularly autoregressive (AR) frameworks, primarily rely on straightforward temporal sequencing of multi-channel EEG data, which fails to capture the rich physiological characteristics inherent to EEG signals. Moreover, their time-centered modeling approach also limits the effective representation of the dynamic spatial topology of brain activity. To address these challenges and fully exploit the potential of large-scale EEG models, we propose a novel Topology Hierarchical Derived Brain Autoregressive Modeling (THD-BAR) for EEG generic representations. The core innovation of THD-BAR lies in the introduction of the Brain Topology Hierarchy (BTH), which establishes a multi-scale spatial order for EEG channels. This hierarchical structure enables a redefinition of autoregressive learning as a "next-scale-time prediction" problem, effectively capturing both spatial and temporal dynamics. Based on BTH, we design a Topology-Hierarchical Vector Quantized-Variational Autoencoder (THVQ-VAE) for multi-scale tokenization and develop an enhanced Brain Autoregressive (BAR) module with specialized masking strategies for prediction. Through extensive large-scale pre-training on 17 datasets, followed by rigorous validation on 10 downstream datasets spanning 5 distinct tasks, THD-BAR consistently outperforms existing methods. These results highlight the superior generalization and modeling capabilities of our proposed approach.

Zhihong Liu, Jiajia Liu, Yong Zeng et al.

Covert communication can prevent the adversary from knowing that a wireless transmission has occurred. In the additive white Gaussian noise channels, a square root law is obtained and the result shows that Alice can reliably and covertly transmit $\mathcal{O}(\sqrt{n})$ bits to Bob in $n$ channel uses. If additional "friendly" node near the adversary can inject artificial noise to aid Alice in hiding her transmission attempt, covert throughput can be improved, i.e., Alice can covertly transmit $\mathcal{O}(\min\{n,λ^{α/2}\sqrt{n}\})$ bits to Bob over $n$ uses of the channel ($λ$ is the density of friendly nodes and $α$ is the path loss exponent of wireless channels). In this paper, we consider the covert communication in a noisy wireless network, where Bob and the adversary Willie not only experience the background noise, but also the aggregated interference from other transmitters. Our results show that uncertainty in interference experienced by Willie is beneficial to Alice. When the distance between Alice and Willie $d_{a,w}=ω(n^{δ/4})$ ($δ=2/α$ is stability exponent), Alice can reliably and covertly transmit $\mathcal{O}(\log_2\sqrt{n})$ bits to Bob in $n$ channel uses. Although the covert throughput is lower than the square root law and the friendly jamming scheme, the spatial throughput is higher. From the network perspective, the communications are hidden in "the sound and the fury" of noisy wireless networks, and what Willie sees is merely a "shadow" wireless network. He knows for certain that some nodes are transmitting, but he cannot catch anyone red-handed.