Neural decoding from electroencephalography (EEG) remains fundamentally limited by poor generalization to unseen subjects, driven by high inter-subject variability and the lack of large-scale datasets to model it effectively. Existing methods often rely on synthetic subject generation or simplistic data augmentation, but these strategies fail to scale or generalize reliably. We introduce \textit{MultiDiffNet}, a diffusion-based framework that bypasses generative augmentation entirely by learning a compact latent space optimized for multiple objectives. We decode directly from this space and achieve state-of-the-art generalization across various neural decoding tasks using subject and session disjoint evaluation. We also curate and release a unified benchmark suite spanning four EEG decoding tasks of increasing complexity (SSVEP, Motor Imagery, P300, and Imagined Speech) and an evaluation protocol that addresses inconsistent split practices in prior EEG research. Finally, we develop a statistical reporting framework tailored for low-trial EEG settings. Our work provides a reproducible and open-source foundation for subject-agnostic EEG decoding in real-world BCI systems.

翻译：基于脑电图（EEG）的神经解码仍受限于对未见受试者的泛化能力不足，这主要由高受试者间变异性以及缺乏大规模数据集来有效建模所致。现有方法通常依赖于合成受试者生成或简化的数据增强策略，但这些方法难以扩展或实现可靠的泛化。本文提出 MultiDiffNet，一种基于扩散的框架，它通过为多目标优化学习一个紧凑的潜在空间，完全绕过了生成式数据增强。我们直接从该空间进行解码，并在受试者与会话分离的评估设置下，于多种神经解码任务中实现了最先进的泛化性能。此外，我们整理并发布了一个统一的基准测试套件，涵盖四个复杂度递增的 EEG 解码任务（SSVEP、运动想象、P300 和想象语音），以及一个旨在解决先前 EEG 研究中数据划分不一致问题的评估协议。最后，我们开发了一个专为低试次 EEG 场景设计的统计报告框架。本研究为现实世界脑机接口系统中的受试者无关 EEG 解码提供了一个可复现且开源的基础。