Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain constrained by three fundamental limitations: Semantic Bias (mode collapse into generic templates), Signal Neglect (hallucination based on linguistic priors rather than neural inputs), and the BLEU Trap, where evaluation metrics are artificially inflated by high-frequency stopwords, masking a lack of true semantic fidelity. To address these challenges, we propose SemKey, a novel multi-stage framework that enforces signal-grounded generation through four decoupled semantic objectives: sentiment, topic, length, and surprisal. We redesign the interaction between the neural encoder and the Large Language Model (LLM) by injecting semantic prompts as Queries and EEG embeddings as Key-Value pairs, strictly forcing the model to attend to neural inputs. Furthermore, we move beyond standard translation metrics by adopting N-way Retrieval Accuracy and Fréchet Distance to rigorously assess diversity and alignment. Extensive experiments demonstrate that our approach effectively eliminates hallucinations on noise inputs and achieves SOTA performance on these robust protocols. Code will be released upon acceptance at https://github.com/xmed-lab/SemKey.

翻译：从非侵入式脑电信号中解码自然语言是一项前景广阔但极具挑战性的任务。然而，当前最先进的模型仍受限于三个根本性缺陷：语义偏差（退化为通用模板的模式坍塌）、信号忽视（基于语言先验而非神经输入的幻觉生成），以及BLEU陷阱——评估指标被高频停用词人为抬高，掩盖了真实语义保真度的缺失。为解决上述问题，我们提出SemKey——一种新型多阶段框架，通过四项解耦语义目标（情感、主题、长度与惊奇度）强制实现基于信号驱动的生成。我们重新设计了神经编码器与大语言模型之间的交互机制，将语义提示作为查询项、脑电嵌入作为键值对注入模型，严格强制模型关注神经输入。此外，我们突破标准翻译指标的局限，采用N路检索准确率与弗雷歇距离来严格评估多样性与对齐性。大量实验证明，本方法能有效消除噪声输入下的幻觉生成，并在这些稳健评估协议上达到最优性能。代码将在接收后于https://github.com/xmed-lab/SemKey 开源。