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Brain-to-text decoding performance gains were an artifact of timing shortcuts, not neural signals, and removing this artifact unlocks substantial improvements with simple methods.
Mamba-based foundation models for MEG outperform transformers in generative fidelity and training efficiency, unlocking longer context modeling of brain activity.
This work introduces Brain2Semantics2Text, a method that reconstructs text through an intermediate semantic embedding space and describes the core principles of the approach, its implementation, and the strategies used to mitigate the challenges of learning a reliable neural-to-semantic mapping.
Practical non-invasive BCIs cannot require days of user calibration, and this benchmark forces models to tackle word-level MEG decoding under strict 10-to-40-minute subject adaptation budgets.
Standard word error rates artificially inflate speech BCI performance by ignoring unmodeled language, masking a critical capability trade-off that an open-vocabulary information-theoretic metric resolves while improving decoding accuracy by up to 16.3%.
Achieving state-of-the-art performance in speech decoding with a dataset that features 80 hours of deep, within-subject MEG data sets a new standard for neural data quality and quantity.
