Enabling natural communication through brain-computer interfaces (BCIs) remains one of the most profound challenges in neuroscience and neurotechnology. While existing frameworks offer partial solutions, they are constrained by oversimplified semantic representations and a lack of interpretability. To overcome these limitations, we introduce Semantic Intent Decoding (SID), a novel framework that translates neural activity into natural language by modeling meaning as a flexible set of compositional semantic units. SID is built on three core principles: semantic compositionality, continuity and expandability of semantic space, and fidelity in reconstruction. We present BrainMosaic, a deep learning architecture implementing SID. BrainMosaic decodes multiple semantic units from EEG/SEEG signals using set matching and then reconstructs coherent sentences through semantic-guided reconstruction. This approach moves beyond traditional pipelines that rely on fixed-class classification or unconstrained generation, enabling a more interpretable and expressive communication paradigm. Extensive experiments on multilingual EEG and clinical SEEG datasets demonstrate that SID and BrainMosaic offer substantial advantages over existing frameworks, paving the way for natural and effective BCI-mediated communication.

翻译：通过脑机接口实现自然交流，仍然是神经科学与神经技术领域最深刻的挑战之一。现有框架虽然提供了部分解决方案，但受限于过度简化的语义表征和可解释性的缺乏。为克服这些局限，我们引入了语义意图解码，这是一个新颖的框架，通过将意义建模为一组灵活的组合性语义单元，将神经活动转化为自然语言。SID建立在三个核心原则之上：语义组合性、语义空间的连续性与可扩展性，以及重建的保真度。我们提出了实现SID的深度学习架构BrainMosaic。BrainMosaic利用集合匹配从脑电/立体定向脑电信号中解码多个语义单元，然后通过语义引导的重建来重构连贯的句子。这种方法超越了依赖固定类别分类或无约束生成的传统流程，实现了一种更具可解释性和表达力的交流范式。在多语言脑电和临床立体定向脑电数据集上进行的大量实验表明，SID和BrainMosaic相较于现有框架具有显著优势，为自然有效的脑机接口介导的交流铺平了道路。