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.
These models aim to reconstruct stimuli or intentions based on measured neural activity [1]
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.