A two-stage model aligns intracranial EEG signals with text embeddings and reconstructs the semantic content of perceived speech from as little as 30 minutes of neural data.
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Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEG
A two-stage model aligns intracranial EEG signals with text embeddings and reconstructs the semantic content of perceived speech from as little as 30 minutes of neural data.