用扩散模型实现跨被试脑信号解码，提升通用性。
MultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decoding
- 设计多目标扩散框架，直接在紧凑潜在空间解码
- 在四个任务上达到当前最佳跨被试性能
- 提供统一基准和低试验场景报告框架，适合脑机接口研究
从脑电图（EEG）进行神经解码仍受限于对未见被试的泛化能力差，主要由个体间差异大及缺乏大规模数据集建模所致。现有方法常依赖合成被试生成或简单数据增强，但难以扩展且泛化不稳定。我们提出MultiDiffNet，一种基于扩散的框架，通过学习一个优化的紧凑潜在空间来避免生成式增强，直接在此空间中解码，实现了在多种神经解码任务中的最先进跨被试性能。我们还构建并发布了涵盖四种逐步复杂度的EEG解码任务（SSVEP、运动想象、P300、想象语音）的统一基准套件，并制定解决先前研究分裂不一致问题的评估协议。最后，我们开发了针对低试验量场景的统计报告框架。本工作为真实脑机接口系统中的无被试依赖解码提供了可复现、开源的基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。