用符号规则净化脑电语义，让想象变文字更稳定
SYNAPSE: Neuro-Symbolic Visual Thought-to-Text Decoding via Topological Semantic Denoising
- 用常识图谱和潜在样本净化脑电信号生成的语义候选
- 在多个脑电解码基准上超越无约束提示基线，媲美微调模型
- 无需微调大模型，保护隐私且抗干扰能力强
近年来大型语言模型的发展加速了开放词汇的脑电到想象文本解码，将视觉感知时记录的非侵入性神经活动转化为所视刺激的连贯自然语言描述。然而现有系统对生物噪声高度敏感，受污染的神经投影会导致冻结语言模型产生幻觉或语义不稳。我们提出SYNAPSE（符号神经对齐以精确提取语义），一种轻量级神经符号框架，通过推理时的符号正则化稳定神经文本生成。利用常识图谱结构和隐式原型净化从脑电信号衍生的语义候选，SYNAPSE 在不进行端到端大模型微调的情况下提升语义稳定性。跨主流脑电解码基准及多种冻结语言模型后端的实验表明，该方法持续优于无约束提示基线，在物体标签缺失条件下仍具鲁棒性，性能可媲美资源消耗显著更高的微调系统，同时通过将原始脑电信号处理完全置于编码器堆栈内，保障生物特征隐私。
原文摘要 · Abstract (English)
Recent advances in large language models have accelerated open-vocabulary EEG-to-imagined-text decoding, where non-invasive neural activity recorded during visual perception is translated into coherent natural language descriptions of viewed stimuli. However, existing systems remain highly vulnerable to biological noise, where corrupted neural projections induce hallucinated or semantically unstable generation in frozen language models. We introduce SYNAPSE (Symbolic Neural Alignment for Precise Semantic Extraction), a lightweight neuro-symbolic framework that stabilizes neural text generation through inference-time symbolic regularization. By purifying EEG-derived semantic candidates using commonsense graph structure and latent exemplars, SYNAPSE improves semantic stability without end-to-end LLM fine-tuning. Experiments across popular EEG decoding benchmarks and multiple frozen LLM backends demonstrate consistent gains over unconstrained prompting baselines, robustness under object-label ablation, and performance commensurate with substantially more resource-intensive fine-tuned systems, while preserving biometric privacy by localizing raw EEG processing entirely within the encoder stack.
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