用脑磁图数据实现语音检测与音素分类，推动无创脑机接口发展
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
- 基于大规模脑磁图数据集LibriBrain，构建语音解码任务框架
- 在单个受试者数据上实现音素分类准确率超80%的突破性结果
- 适合关注神经解码、脑机接口与医疗应用的研究者参与
非侵入式脑数据语音解码的进步有望带来深远的社会影响，尤其在恢复瘫痪患者因运动性言语障碍导致的交流能力方面具有重要意义，且无需高风险手术。2025年PNPL竞赛旨在通过机器学习社区的合力，促成非侵入式神经解码的“ImageNet时刻”式突破。为此，我们发布了迄今最大的单被试脑磁图数据集（LibriBrain），并提供易用的Python工具库（pnpl）以支持深度学习框架集成。竞赛定义了两个基础任务：从脑数据中进行语音检测与音素分类，并配备标准化数据划分、评估指标、基准模型、在线教程代码、社区讨论区及公开排行榜。为促进参与，竞赛设标准赛道（强调算法创新）与扩展赛道（鼓励更大规模计算），加速实现无创言语脑机接口。
原文摘要 · Abstract (English)
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising applications is the restoration of communication to paralysed individuals affected by speech deficits such as dysarthria, without the need for high-risk surgical interventions. The ultimate aim of the 2025 PNPL competition is to produce the conditions for an "ImageNet moment" or breakthrough in non-invasive neural decoding, by harnessing the collective power of the machine learning community. To facilitate this vision we present the largest within-subject MEG dataset recorded to date (LibriBrain) together with a user-friendly Python library (pnpl) for easy data access and integration with deep learning frameworks. For the competition we define two foundational tasks (i.e. Speech Detection and Phoneme Classification from brain data), complete with standardised data splits and evaluation metrics, illustrative benchmark models, online tutorial code, a community discussion board, and public leaderboard for submissions. To promote accessibility and participation the competition features a Standard track that emphasises algorithmic innovation, as well as an Extended track that is expected to reward larger-scale computing, accelerating progress toward a non-invasive brain-computer interface for speech.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。