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.

翻译：数据高效的神经解码是语音脑机接口面临的核心挑战。本研究首次展示了基于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。我们在50小时单被试听觉数据上预训练基于Conformer的模型，随后仅使用18名被试每人5分钟的数据进行微调。迁移学习带来了持续的性能提升：任务内准确率提高1-4%，跨任务准确率提升更为显著，达到5-6%。预训练不仅提升了各任务内部性能，更实现了感知与产生任务间的可靠跨任务解码。关键发现表明，基于语音产生任务训练的模型能够以高于随机水平的准确率解码被动听觉任务，这证实了学习到的表征反映了共享的神经加工过程，而非任务特定的运动活动。