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 pre-train 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.
699d7984c37da633d74f492c
Xabier de Zuazo
claimed_verified
2026-02-27T16:44:27.030Z
621ff334fa5492893dc03d82
https://cdn-avatars.huggingface.co/v1/production/uploads/621ff334fa5492893dc03d82/EAIr-l3O4OeM10f1boLux.jpeg
Xabier de Zuazo
zuazo
user
zuazo
699d7984c37da633d74f492d
Vincenzo Verbeni
699d7984c37da633d74f492e
Eva Navas
699d7984c37da633d74f492f
Ibon Saratxaga
699d7984c37da633d74f4930
Mathieu Bourguignon
699d7984c37da633d74f4931
Nicola Molinaro
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 pre-train 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.
Transfer learning enables efficient MEG-based speech decoding from perception to production tasks using a Conformer model with minimal fine-tuning data.
Conformer-based model transfer learning cross-task decoding MEG-based speech models pre-training fine-tuning