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.
arXiv:2604. 16370v2 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth.
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.
arXiv:2609.24095v1 Announce Type: new Abstract: While deep learning has enabled language decoding from intracranial brain recordings, extending this capability to non-invasive recordings remains an u...
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.
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.
arXiv:2607. 25626v1 Announce Type: new Abstract: Decoding speech information directly from scalp electroencephalography (EEG) into text provides a potential non-invasive neural communication pathway for individuals with severe speech and motor impairments.
The study evaluated whether imagined speech decoded from scalp EEG can transfer across individuals using three public datasets. Across 482 participant pairs, cross‑subject decoding accuracy was essentially at chance, while within‑subject decoding only surpassed chance in one dataset. The results suggest that imagined‑speech content does not generalize between people and that larger single‑condition datasets are more beneficial than further model development.
While deep learning has enabled language decoding from intracranial brain recordings, extending this capability to non-invasive recordings remains an unresolved challenge. Decoding individual words fr...
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding introduces a model‑agnostic framework that adaptively aligns EEG signals with visual semantics. It replaces fixed visual or textual anchors with EEG‑aware class‑level contrastive supervision and employs structure‑consistent interpolation to preserve channel‑wise and temporal importance. Across multiple evaluation settings—including subject‑dependent, subject‑independent, strict cross‑subject transfer, and continual adaptation—ProCA delivers significant performance gains, achieving relative Top‑1 improvements ranging from 7.4% to 28.1%.
The study shows that previously reported gains in decoding spoken words from non‑invasive brain recordings can largely be replicated without any brain data, due to a timing shortcut where overlapping windows reveal word durations. By processing each window independently rather than jointly, the authors eliminate this shortcut, enabling the neural network to learn genuine brain‑based word information. This change improves performance and makes two existing strategies—aggregating predictions from multiple neural responses and incorporating a pretrained language model—significantly more effective, achieving a word error rate of 36.6% on a perceived speech benchmark.
arXiv:2603. 17109v2 Announce Type: replace Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction.
arXiv:2607. 18749v1 Announce Type: new Abstract: Translating brain signals into text could restore communication for people with severe paralysis, yet practically usable systems to date rely on invasive electrocorticography (ECoG).