Cross-subject decoding of human neural data for speech Brain Computer Interfaces
Boccato, T.; Olak, M. R.; Ferrante, M.
Show 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis 97%
- Modeling neural coding in the auditory midbrain with high resolution and accuracy 94%
- Parallel hierarchical encoding of linguistic representations in the human auditory cortex and recurrent automatic speech recognition systems 93%
Similar papers in this journal
- Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS 95%
- Bridging Auditory Perception and Natural Language Processing with Semantically informed Deep Neural Networks 95%
- BioCPPNet: Automatic Bioacoustic Source Separation with Deep Neural Networks 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.