MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
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.
692ef001bfc6eea1b6fb68cd
Xabier de Zuazo
claimed_verified
2025-12-12T09:18:03.373Z
621ff334fa5492893dc03d82
https://cdn-avatars.huggingface.co/v1/production/uploads/621ff334fa5492893dc03d82/EAIr-l3O4OeM10f1boLux.jpeg
Xabier de Zuazo
zuazo
user
zuazo
692ef001bfc6eea1b6fb68ce
Ibon Saratxaga
692ef001bfc6eea1b6fb68cf
Eva Navas
MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
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.
The Conformer-based decoders adapted for MEG signals achieved high performance in Speech Detection and Phoneme Classification tasks using task-specific augmentations and normalization techniques.
Conformer MEG SpecAugment inverse-square-root class weighting dynamic grouping loader instance-level normalization F1-macro