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

2026-02-20

AI Summary

This study addresses the challenges of low data efficiency and cross-task decoding in magnetoencephalography (MEG)-based speech brain–computer interfaces by proposing a Conformer-based transfer learning framework. The model is pretrained on large-scale auditory data and fine-tuned with only five minutes of individual-specific data, enabling effective cross-task transfer between speech perception and production tasks. This work provides the first empirical evidence for shared neural representations between these two task types, overcoming the conventional reliance on task-specific motor signals. Experimental results demonstrate performance improvements of 1–4% in within-task decoding accuracy and 5–6% in cross-task scenarios. Notably, models trained on speech production data successfully decode passive auditory perception significantly above chance level, highlighting the framework’s robust generalization capability across distinct cognitive tasks.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Speech

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• MEG
• speech decoding
• transfer learning
• cross-task
• data efficiency

Innovation

• MEG
• transfer learning
• cross-task decoding
• Conformer
• speech brain-computer interface