Decoding the decoder: Contextual sequence-to-sequence modeling for intracortical speech decoding

ArXi:2603.20246v1 Announce Type: cross Speech brain--computer interfaces require decoders that translate intracortical activity into linguistic output while remaining robust to limited data and day-to-day variability. While prior high-performing systems have largely relied on framewise phoneme decoding combined with downstream language models, it remains unclear what contextual sequence-to-sequence decoding contributes to sublexical neural readout, robustness, and interpretability.