title: Neuro2Semantic: A Transfer Learning Framework for Semantic
    Reconstruction of Continuous Language from Human Intracranial EEG
summary: Decoding continuous language from neural signals remains a significant
  challenge in the intersection of neuroscience and artificial intelligence. We
  introduce Neuro2Semantic, a novel framework that reconstructs the semantic
  content of perceived speech from intracranial EEG (iEEG) recordings. Our
  approach consists of two phases: first, an LSTM-based adapter aligns neural
  signals with pre-trained text embeddings; second, a corrector module generates
  continuous, natural text directly from these aligned embeddings. This flexible
  method overcomes the limitations of previous decoding approaches and enables
  unconstrained text generation. Neuro2Semantic achieves strong performance with
  as little as 30 minutes of neural data, outperforming a recent state-of-the-art
  method in low-data settings. These results highlight the potential for
  practical applications in brain-computer interfaces and neural decoding
  technologies.
paper_authors._id: 683f147e791f5015dfcb644a
paper_authors.name: Siavash Shams
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T09:01:49.892Z
paper_authors._id: 6637d9685eedec13fcbd1f87
paper_authors.avatarUrl: /avatars/a72d4e289c28b4d91757241d7b25985d.svg
paper_authors.fullname: Siavash
paper_authors.type: user
paper_authors.user: attentionisallyouneed369
paper_authors._id: 683f147e791f5015dfcb644b
paper_authors.name: Richard Antonello
paper_authors._id: 683f147e791f5015dfcb644c
paper_authors.name: Gavin Mischler
paper_authors._id: 683f147e791f5015dfcb644d
paper_authors.name: Stephan Bickel
paper_authors._id: 683f147e791f5015dfcb644e
paper_authors.name: Ashesh Mehta
paper_authors._id: 683f147e791f5015dfcb644f
paper_authors.name: Nima Mesgarani
paper_title: Neuro2Semantic: A Transfer Learning Framework for Semantic
    Reconstruction of Continuous Language from Human Intracranial EEG
paper_summary: Decoding continuous language from neural signals remains a significant
  challenge in the intersection of neuroscience and artificial intelligence. We
  introduce Neuro2Semantic, a novel framework that reconstructs the semantic
  content of perceived speech from intracranial EEG (iEEG) recordings. Our
  approach consists of two phases: first, an LSTM-based adapter aligns neural
  signals with pre-trained text embeddings; second, a corrector module generates
  continuous, natural text directly from these aligned embeddings. This flexible
  method overcomes the limitations of previous decoding approaches and enables
  unconstrained text generation. Neuro2Semantic achieves strong performance with
  as little as 30 minutes of neural data, outperforming a recent state-of-the-art
  method in low-data settings. These results highlight the potential for
  practical applications in brain-computer interfaces and neural decoding
  technologies.
paper_ai_summary: Neuro2Semantic reconstructs semantic content from neural signals using LSTM-based alignment and text generation, outperforming existing methods with limited data.
paper_ai_keywords: iEEG LSTM align text embeddings corrector module neural decoding brain-computer interfaces