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

Tommaso Boccato¹, Michal Olak¹, Matteo Ferrante¹

¹Tether Evo, San Salvador, El Salvador.

Journal of Neural Engineering

|July 2, 2026

Summary

This study introduces a novel neural-to-phoneme decoder for brain-to-text systems, achieving high performance across different participants. This breakthrough advances brain-computer interfaces (BCIs) by enabling scalable speech decoding without single-subject training.

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Area of Science:

Neuroscience
Computational Linguistics
Biomedical Engineering

Background:

Brain-to-text systems show promise but struggle with cross-subject generalization.
Current models are typically trained on single-participant data, limiting their applicability.

Purpose of the Study:

To develop the first neural-to-phoneme decoder capable of joint training on multiple large-scale intracortical speech datasets.
To investigate and enable cross-subject generalization in brain-to-text systems.

Main Methods:

Jointly trained a hierarchical GRU decoder on two large intracortical speech datasets.
Introduced day- and dataset-specific affine transforms for neural activity alignment.
Incorporated intermediate CTC supervision and feedback connections to address CTC loss assumptions.

Keywords:

BCI brain decoding cross-subject speech

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Main Results:

The decoder matched or surpassed within-subject performance while trained across participants.
The model adapted to new subjects with linear transforms or minimal fine-tuning.
Initial generalization to an independent inner-speech dataset was observed.

Conclusions:

Cross-subject pretraining is a viable strategy for enhancing the scalability of speech brain-computer interfaces (BCIs).
The proposed decoder architecture and alignment techniques facilitate robust cross-subject generalization.