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 (Willett et al. 2023; Card et al. 2024), 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 (Kunz et al. 2025), 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 BCIs.