A cross-species neural foundation model for end-to-end speech decoding

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This paper presents a neural foundation model approach for decoding attempted or imagined speech directly from neural activity into text, moving beyond cascaded phoneme-to-language pipelines.

The system combines cross-task and cross-species neural pretraining with language-model alignment, making the decoding pipeline more end-to-end and more transferable across limited neural datasets.

Its relevance to AI is direct: it treats neural data as another sequence modality that can be aligned with foundation-model representations, while keeping the BCI objective clinically meaningful.

For the NeuroAI/BCI track, it is a strong example of foundation-model methodology reshaping brain-to-language interfaces.

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