Phoneme- vs. Character-Level Targets and Selective State-Space Models for Intracortical Brain-to-Text
State-of-the-art intracortical brain-to-text systems pair a neural-sequence phone decoder with an external language model. Two design axes remain underexplored: whether selective state-space models (Mamba) improve on recurrent decoders, and how the output target (phonetic vs.\ character) interacts with that choice. On the public Brain-to-Text '25 benchmark, we study a controlled 2x2 grid (GRU vs.\ hybrid Mamba decoder; phonetic vs.\ character targets) trained with a CTC objective under one reproducible protocol. The recurrent baseline remains strongest: the best phonetic GRU reaches 12.62\% PER and 21.19\% WER, while the best textual GRU after LM rescoring reaches 13.39\% CER and 26.28\% ...
| Authors | Lucas Zamora Vera, Jose A. Gonzalez-Lopez |
|---|---|
| Primary category | cs.CL |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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