MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification

• URL: http://arxiv.org/abs/2512.01443v1
• Date: Mon, 01 Dec 2025 09:25:22 GMT
• Title: MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
• Abstract summary: We present Conformer-based decoders for the LibriBrain 2025 PNPL competition.<n>Our approach adapts a compact Conformer to raw 306-channel MEG signals.<n>For Speech Detection, a MEG-oriented SpecAugment provided a first exploration of MEG-specific augmentation.<n>For Phoneme Classification, we used inverse-square-root class weighting and a dynamic grouping loader.
• Score: 1.0896567381206717
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Conformer-based decoders for the LibriBrain 2025 PNPL competition, targeting two foundational MEG tasks: Speech Detection and Phoneme Classification. Our approach adapts a compact Conformer to raw 306-channel MEG signals, with a lightweight convolutional projection layer and task-specific heads. For Speech Detection, a MEG-oriented SpecAugment provided a first exploration of MEG-specific augmentation. For Phoneme Classification, we used inverse-square-root class weighting and a dynamic grouping loader to handle 100-sample averaged examples. In addition, a simple instance-level normalization proved critical to mitigate distribution shifts on the holdout split. Using the official Standard track splits and F1-macro for model selection, our best systems achieved 88.9% (Speech) and 65.8% (Phoneme) on the leaderboard, surpassing the competition baselines and ranking within the top-10 in both tasks. For further implementation details, the technical documentation, source code, and checkpoints are available at https://github.com/neural2speech/libribrain-experiments.

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