← Back to all news
arXiv Machine Learning By Ingo Marquardt, Anthilia Alchanat, Priyanka Jain

Decoding silent reading from non-invasive EEG

Read the original on arXiv Machine Learning →

arXiv:2608. 20186v1 Announce Type: new 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
3d ago

Deep LLM layers expose passage-level context in EEG during silent reading

The study investigates how information about words and passage context is represented across the layers of the Llama‑3.1‑8B language model during silent reading, using EEG recordings. Decoders trained on each layer show that word‑level decodability peaks in early layers and diminishes toward the final layer, while a context‑tracking component increases with depth. Even a single 0.5‑second EEG window can capture passage‑level information, and control experiments confirm that this context tracking is driven by the text rather than by word decoding or session artifacts.

By Ingo Marquardt, Anthilia Alchanat, Priyanka Jain
llmsrag
More like this →
arXiv Machine Learning
6d ago

Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech

The paper introduces a multimodal contrastive learning framework that aligns EEG recordings of imagined speech with language representations from a frozen text encoder. By training an EEG encoder with a composite objective that balances semantic reweighting and class discrimination, the method preserves separability among 39 intent categories while embedding EEG signals into a language embedding space. Experiments on the Chisco dataset show comparable decoding performance to a task‑optimized classifier, and additional metrics such as MaxCosine and semantic axes demonstrate intrinsic reliability and broader semantic structure beyond fixed categories.

By Shahryar G. Namdari, Francesco Iacomi, Riccardo Barbieri, Luca Mainardi
ragmultimodalsafety
More like this →
arXiv AI
Jul 24

Transition-Related Potentials as Markers of Narrative Comprehension in Continuous EEG

arXiv:2607. 20720v1 Announce Type: cross Abstract: Harnessing the potential of electroencephalography (EEG) for brain research is fundamentally limited by intrinsic noise and the diffuse projection of brain-generated activity over the scalp.

By B\'alint Csan\'ady, P\'eter Vedres, Krist\'of Zsolt Mak\'o, Orsolya Papp-Zipernovszky, M\'arta Volosin, D\'avid Apagyi, Andr\'as Luk\'acs, Andr\'as B\'alint Kov\'acs, Zoltan Nadasdy
More like this →