Neuro2Semantic: A Transfer Learning Framework for Semantic
  Reconstruction of Continuous Language from Human Intracranial EEG
Authors: name: Siavash Shams, name: Richard Antonello, name: Gavin Mischler, name: Stephan Bickel, name: Ashesh Mehta, name: Nima Mesgarani

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.