从ImageNet迁移学习用于基于MEG的想象言语解码
Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech
AI总结：
本文提出利用ImageNet预训练模型解码想象言语，通过转换MEG信号为时间-频率表示，提升非侵入性解码性能。
摘要：
非侵入性解码想象言语仍具有挑战性，因为信号弱且分布广泛，且标注数据有限。本文介绍了一种基于图像的方法，将磁脑图（MEG）信号转换为与预训练视觉模型兼容的时间-频率表示。来自21名参与者进行想象言语任务的MEG数据通过可学习的传感器空间卷积投影到三个空间频谱混合中，产生紧凑的图像样输入以供ImageNet预训练的视觉架构使用。这些模型优于经典和非预训练模型，在想象 vs. 静默、想象言语 vs. 静默阅读、元音解码中分别达到90.4%、81.0%和60.6%的平衡准确率。跨受试者评估证实预训练模型捕捉到共享的神经表示，时间分析将判别信息局部化到想象锁定区间。这些发现表明，将预训练视觉模型应用于基于图像的MEG表示可以有效捕捉非侵入性神经信号中想象言语的结构。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
