用少量数据实现脑磁图语音解码的跨任务迁移，提升识别准确率。
MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data
- 用50小时听觉数据预训练，仅需5分钟/人微调，实现高效迁移。
- 任务内准确率提升1-4%，跨任务最高提升5-6%。
- 证明语音感知与产生共享神经表征，适合小样本脑机接口研究。
数据高效的神经解码是语音脑机接口的核心挑战。我们首次展示了基于脑磁图（MEG）的语音模型在感知与产生任务间的迁移学习与跨任务解码。使用50小时单人听觉数据预训练一个基于Conformer的模型，并在18名受试者每人仅5分钟的数据上进行微调。迁移学习带来稳定性能提升：任务内准确率提高1-4%，跨任务提升高达5-6%。预训练不仅改善了各任务内的表现，还实现了感知与产生之间的可靠跨任务解码。关键发现是：语音产生任务训练的模型能高于随机水平解码被动听觉，表明所学表征反映的是共享的神经过程，而非任务特异性的运动活动。
原文摘要 · Abstract (English)
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.
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