Title: Cross-subject decoding of human neural data for speech Brain Computer Interfaces
Primary area: applications to neuroscience & cognitive science
Keywords: BCI, neuroAI, language decoding, invasive neural recordings

Abstract: Brain-to-text systems have recently achieved impressive performance when trained on single-participant data,  but remain limited by uninvestigated cross-subject generalization. 
We present the first neural-to-phoneme decoder trained jointly on the two largest intracortical speech datasets  (Willett et al. 2023; Card et al. 2024),  introducing day- and dataset-specific affine transforms to align neural activity into a shared space. 
A hierarchical GRU decoder with intermediate CTC supervision and feedback connections further mitigates  the conditional-independence assumption of standard CTC loss.  Our model matches or outperforms within-subject baselines while being trained across participants,  and adapts to unseen subjects using only a linear transform or brief fine-tuning. 
On an independent inner-speech dataset (Kunz et al. 2025), our approach demonstrate generalization, by training only subject day specific transforms. These results highlight cross-subject pretraining as a practical path toward 
scalable and clinically deployable speech BCIs.

TL;DR: we demonstrate that cross-subject training of models for speech decoding is feasible and generalization to new subject as well.