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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...
arXiv:2608. 01481v1 Announce Type: new Abstract: Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2.
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.
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.
Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2. 0 audio embeddings.
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.