{"ID":2834406,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01443","arxiv_id":"2512.01443","title":"MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification","abstract":"Decoding speech-related information from non-invasive MEG is a key step toward scalable brain-computer interfaces. We present compact Conformer-based decoders on the LibriBrain 2025 PNPL benchmark for two core 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, winning the Phoneme Classification Standard track. For further implementation details, the technical documentation, source code, and checkpoints are available at https://github.com/neural2speech/libribrain-experiments.","short_abstract":"Decoding speech-related information from non-invasive MEG is a key step toward scalable brain-computer interfaces. We present compact Conformer-based decoders on the LibriBrain 2025 PNPL benchmark for two core tasks: Speech Detection and Phoneme Classification. Our approach adapts a compact Conformer to raw 306-channel...","url_abs":"https://arxiv.org/abs/2512.01443","url_pdf":"https://arxiv.org/pdf/2512.01443v2","authors":"[\"Xabier de Zuazo\",\"Ibon Saratxaga\",\"Eva Navas\"]","published":"2025-12-01T09:25:22Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\",\"cs.NE\",\"cs.SD\"]","methods":"[\"LoRA\"]","has_code":false,"code_links":[{"ID":606412,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2834406,"paper_url":"https://arxiv.org/abs/2512.01443","paper_title":"MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification","repo_url":"https://github.com/neural2speech/libribrain-experiments","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}