AI RESEARCH
Measuring Robustness of Speech Recognition from MEG Signals Under Distribution Shift
arXiv CS.LG
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ArXi:2604.04129v1 Announce Type: cross This study investigates robust speech-related decoding from non-invasive MEG signals using the LibriBrain phoneme-classification benchmark from the 2025 PNPL competition. We compare residual convolutional neural networks (CNNs), an STFT-based CNN, and a CNN--Transformer hybrid, while also examining the effects of group averaging, label balancing, repeated grouping, normalization strategies, and data augmentation.