MEGConformer：基于Conformer的MEG解码器，用于鲁棒的语音和音素分类 / MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification
1️⃣ 一句话总结
这篇论文提出了一种基于Conformer架构的模型，能够直接从脑磁图信号中有效识别出人是否在听语音以及具体听到的是哪个音素，在两项关键任务上都超越了比赛基准并取得了优异的成绩。
🤖 系统
Abstract - 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 this https URL.