Decoding silent reading from non-invasive EEG

View PDF HTML (experimental) Abstract: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. Comments: 45 pages (including 12 pages of Supplementary Material), 11 figures (including 2 in Supplementary Material) Subjects: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC) Cite as: arXiv:2608.20186 [cs.LG] (or arXiv:2608.20186v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.20186 arXiv-issued DOI via DataCite (pending registration) Submission history From: Ingo Marquardt [view email] [v1] Thu, 20 Aug 2026 15:41:05 UTC (4,020 KB)