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arXiv · 2601.15909

Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech

Soufiane Jhilal·Stéphanie Martin·Anne-Lise Giraud
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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.

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Soufiane Jhilal, Stéphanie Martin, Anne-Lise Giraud. 2026-01-22. Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech. https://arxiv.org/abs/2601.15909

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