The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset

The advance of speech decoding from non-invasive brain data holds the potential forprofound societal impact. Among its most promising applications is the restorationof communication to paralysed individuals affected by speech deficits such asdysarthria, without the need for high-risk surgical interventions. The ultimateaim of the 2025 PNPL competition is to produce the conditions for an “ImageNetmoment” or breakthrough in non-invasive neural decoding, by harnessing thecollective power of the machine learning community.To facilitate this vision we present the largest within-subject MEG dataset recordedto date (LibriBrain) together with a user-friendly Python library (pnpl) for easydata access and integration with deep learning frameworks. For the competitionwe define two foundational tasks (Speech Detection and Phoneme Classificationfrom 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 participationthe 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.