发表机构
nubrain（nubrain）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以默读为替代任务，用CLIP式对比训练的卷积EEG编码器结合因果Transformer，从EEG中成功解码开放词汇单词级信息，发现解码受数据限制而非饱和。
AI 中文摘要
无创内隐言语解码面临一个根本数据问题：无法收集脑活动与个体自发内隐独白配对的语料库，而可用的替代范式（提示式重复及回溯报告的生成式内隐言语）获取缓慢、时间锁定性差且受试者依从性无法验证。因此，我们将默读作为可扩展的替代任务，探究对比解码器能从中提取多少词汇和语义信息。我们报告了一项开放词汇分析，基于单名参与者在393轮（约49小时）19通道干电极EEG记录的约24万个单词呈现数据。连续叙事文本中的单词以快速序列视觉呈现，每次试验随机化排版以部分解除单词身份与低级视觉形式的关联。卷积EEG编码器（可选后接因果Transformer）采用CLIP式对比目标训练，使短EEG窗口与大型语言模型提取的呈现单词的隐藏状态嵌入对齐。解码以单词分组的前10名检索（针对置换基线）评估，结果显著高于随机水平，可扩展至中频和稀有单词，且随训练数据量呈对数线性增长，无饱和迹象。移除枕叶和后颞叶电极使单词级增益降低约三分之一，但上下文跟踪未受影响。控制分析将单词级解码与叙事上下文跟踪及Transformer位置嵌入引入的非神经位置先验分离。这些结果表明，默读期间EEG可恢复开放词汇单词级信息，且解码受数据限制而非饱和。
英文摘要
Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable. We therefore treat silent reading as a scalable proxy task and ask how much lexical and semantic information a contrastive decoder can extract from it. We report an open-vocabulary analysis of approximately 240,000 word presentations recorded from a single densely-sampled participant across 393 runs (ca. 49 h) of 19-channel dry-electrode EEG. Words from continuous narrative text were presented in rapid serial visual presentation, with typography randomised on every trial to partially decorrelate word identity from low-level visual form. A convolutional EEG encoder, optionally followed by a causal transformer, was trained with a CLIP-style contrastive objective to align short EEG windows with hidden-state embeddings of the presented word taken from a large language model. Decoding, evaluated as word-grouped top-10 retrieval against permutation baselines, was reliably above chance, extended to mid-frequency and rare words, and scaled log-linearly with training-data volume with no sign of saturation. Removing occipital and posterior-temporal electrodes reduced the word-level gain by roughly one third but left context tracking unchanged. Control analyses separate word-level decoding from narrative context tracking and from a non-neural positional prior introduced by the transformer's positional embedding. These results establish that open-vocabulary word-level information is recoverable from EEG during silent reading, and that decoding is data-limited rather than saturated.
Comments45 pages (including 12 pages of Supplementary Material), 11 figures (including 2 in Supplementary Material)
