Decoding linguistically meaningful representations from non-invasive neural recordings remains a central challenge in neural speech decoding. Among available neuroimaging modalities, magnetoencephalography (MEG) provides a safe and repeatable means of mapping speech-related cortical dynamics, yet its low signal-to-noise ratio and high temporal dimensionality continue to hinder robust decoding. In this work, we introduce MEGState, a novel architecture for phoneme decoding from MEG signals that captures fine-grained cortical responses evoked by auditory stimuli. Extensive experiments on the LibriBrain dataset demonstrate that MEGState consistently surpasses baseline model across multiple evaluation metrics. These findings highlight the potential of MEG-based phoneme decoding as a scalable pathway toward non-invasive brain-computer interfaces for speech.

翻译：从非侵入式神经记录中解码具有语言学意义的表征，仍是神经语音解码领域的核心挑战。在现有的神经影像模态中，脑磁图（MEG）为映射语音相关的皮层动态提供了一种安全且可重复的手段，但其低信噪比和高时间维度特性持续阻碍着稳健的解码。本研究提出MEGState，一种用于从MEG信号中解码音素的新型架构，该架构能够捕捉听觉刺激引发的细粒度皮层响应。在LibriBrain数据集上进行的大量实验表明，MEGState在多项评估指标上均持续超越基线模型。这些发现凸显了基于MEG的音素解码作为一种可扩展路径，在实现非侵入式语音脑机接口方面的潜力。