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

TL;DR

The paper introduces the 2025 PNPL competition focused on speech detection and phoneme classification from MEG brain data, aiming to advance non-invasive neural decoding for communication restoration in speech-impaired individuals.

Contribution

It provides the largest MEG dataset (LibriBrain), standardized tasks, benchmark models, and a platform to foster innovation in non-invasive speech decoding.

Findings

Largest within-subject MEG dataset to date

Standardized tasks and evaluation metrics established

Community platform with leaderboard and tutorials

Abstract

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.