MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data

Data-efficient neural decoding is a central challenge for speech brain-computer interfaces.We present the first demonstration of transfer learning and cross-task decoding for MEG-based speech models spanning perception and production.We pretrain a Conformer-based model on 50 hours of single-subject listening data and fine-tune on just 5 minutes per subject across 18 participants.Transfer learning yields consistent improvements, with in-task accuracy gains of 1-4% and larger cross-task gains of up to 5-6%.Not only does pre-training improve performance within each task, but it also enables reliable cross-task decoding between perception and production.Critically, models trained on speech production decode passive listening above chance, confirming that learned representations reflect shared neural processes rather than task-specific motor activity.