Translating brain activity into meaningful visual content has long been recognized as a fundamental challenge in neuroscience and brain-computer interface research. Recent advances in EEG-based neural decoding have shown promise, yet two critical limitations remain in this area: poor generalization across subjects and constraints to specific visual tasks. We introduce NEED, the first unified framework achieving zero-shot cross-subject and cross-task generalization for EEG-based visual reconstruction. Our approach addresses three fundamental challenges: (1) cross-subject variability through an Individual Adaptation Module pretrained on multiple EEG datasets to normalize subject-specific patterns, (2) limited spatial resolution and complex temporal dynamics via a dual-pathway architecture capturing both low-level visual dynamics and high-level semantics, and (3) task specificity constraints through a unified inference mechanism adaptable to different visual domains. For video reconstruction, NEED achieves better performance than existing methods. Importantly, Our model maintains 93.7% of within-subject classification performance and 92.4% of visual reconstruction quality when generalizing to unseen subjects, while achieving an SSIM of 0.352 when transferring directly to static image reconstruction without fine-tuning, demonstrating how neural decoding can move beyond subject and task boundaries toward truly generalizable brain-computer interfaces.

Connected vehicles (CVs) utilizing cellular vehicle-to-everything (C-V2X) technology are increasingly coexisting on the road with regular vehicles (RVs). As these CVs interact with each other and with roadside infrastructure through vehicle-vehicle and vehicle-infrastructure cooperation, the characteristics of traffic flow are changing in significant ways. It is therefore crucial to understand how different parameters of CVs, roadside sensors, and V2X communications affect the stability of heterogeneous traffic flow. In this research, we investigate the impact of several transportation and infrastructure parameters on the stability of heterogeneous traffic flow. Specifically, we first examine the effects of traffic density, penetration rate of CVs, detection accuracy of roadside sensors, and time delays in V2X communications. We propose a novel C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture and develop a car-following model based on it. Then, the theoretical stability condition for heterogeneous traffic flow is derived, which reveals the interdependence of transportation and infrastructure parameters. The numerical simulations show that the proposed C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture achieves traffic flow stability at lower CV penetration rates compared to existing studies that only consider vehicle-to-vehicle communications. This finding highlights the importance of leveraging the full potential of C-V2X technology for improving traffic flow stability in real-world settings.