Cross-subject decoding of human neural data for speech brain computer interfaces

Objective.Brain-to-text systems have recently achieved impressive performance when trained on single-participant data, but remain limited by uninvestigated cross-subject generalization.Approach.We present the first neural-to-phoneme decoder trained jointly on the two largest intracortical speech datasets (Willettet al2023Nature6201031-6; Cardet al2024New Engl. J. Med.391609-18), introducing day- and dataset-specific affine transforms to align neural activity into a shared space. Additionally, a hierarchical GRU decoder with intermediate CTC supervision and feedback connections is designed to address the conditional-independence assumption of standard CTC loss.Main results.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 (Kunzet al2025Cell1884658-4673.e17), our approach shows some initial evidence of generalization, by training only subject-, day-specific transforms.Significance.These results demonstrate the feasibility of cross-subject pretraining as a promising direction toward more scalable speech Brain Computer Interfaces.