Reconstructing the brain’s language network from non-invasive EEG enables interpretable speech decoding
This paper presents an interpretable framework that reconstructs brain language network dynamics from non-invasive EEG using neural perturbational inference and cortical eigenmodes to achieve state-of-the-art, physiologically grounded speech decoding with significantly reduced error rates and distinct electrophysiological signatures for error correction.