Abstract
Non-invasive decoding of imagined speech remains challenging due to weak, distributed signals and limited labeled data. Our paper introduces an image-based approach that transforms magnetoencephalography (MEG) signals into time-frequency representations compatible with pretrained vision models. MEG data from 21 participants performing imagined speech tasks were projected into three spatial scalogram mixtures via a learnable sensor-space convolution, producing compact image-like inputs for ImageNet-pretrained vision architectures. These models outperformed classical and non-pretrained models, achieving up to 90.4% balanced accuracy for imagery vs. silence, 81.0% vs. silent reading, and 60.6% for vowel decoding. Cross-subject evaluation confirmed that pretrained models capture shared neural representations, and temporal analyses localized discriminative information to imagery-locked intervals. These findings show that pretrained vision models applied to image-based MEG representations can effectively capture the structure of imagined speech in non-invasive neural signals.
Abstract (translated)
想象中的言语非侵入性解码由于微弱且分布广泛的信号以及有限的标注数据而具有挑战性。我们的论文介绍了一种基于图像的方法，该方法将脑磁图（MEG）信号转换为与预训练视觉模型兼容的时间-频率表示形式。21名参与者在执行想象说话任务时采集的MEG数据通过可学习的传感器空间卷积被投影到三个时空频谱混合体中，产生了适合ImageNet预训练视觉架构的紧凑图像样输入。这些模型超越了传统和未经过预训练的模型，在想象言语与静默之间的分类中达到了最高90.4%的平衡准确率；在想象言语与无声阅读之间达到了81.0%的准确率；以及60.6%的元音解码准确性。跨主体评估证实，预训练模型能够捕捉到共享的神经表示，并且时间分析将区分信息定位到了与想象说话相关的特定时间段内。这些发现表明，当应用于基于图像的MEG表示时，预先训练的视觉模型可以有效地捕获非侵入性神经信号中所含有的想象言语结构。
URL
https://arxiv.org/abs/2601.15909
