Benjamin Ballyk

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.