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.

翻译：我们为LibriBrain 2025 PNPL竞赛提出了基于Conformer的解码器，针对两项基础MEG任务：语音检测与音素分类。我们的方法将紧凑型Conformer适配于原始306通道MEG信号，采用轻量级卷积投影层和任务专用头部。在语音检测任务中，面向MEG的SpecAugment首次探索了MEG专用数据增强技术。对于音素分类，我们使用逆平方根类别加权和动态分组加载器来处理100样本平均示例。此外，简单的实例级归一化被证明对缓解保留集上的分布偏移至关重要。采用官方标准赛道划分和F1-macro进行模型选择，我们的最佳系统在排行榜上分别达到88.9%（语音）和65.8%（音素）的分数，超越了竞赛基线，并在两项任务中均位列前十。更多实现细节、技术文档、源代码及模型检查点可在https://github.com/neural2speech/libribrain-experiments获取。