The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100
The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.
The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.
- LibriBrain100 adds 32 subjects (~40 min each) plus ~80 hours within-subject MEG
- 2025 winners hit 95.6% F1 speech detection and 73.6% phoneme classification
- Broad track cuts subject-specific fine-tuning from ~40 to ~10 minutes
- Aims at non-invasive BCIs restoring communication for profound paralysis
- Products
- LibriBrain100LibriBrain
From the original article
1 min read at huggingface.co
The ambition of the 2025 PNPL competition (Landau et al. , 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Designed to progress from foundational tasks toward the linguistic complexity required for a practical brain-computer interface (BCI), it set the stage with speech detection and phoneme classification tasks.
By Francesco Mantegna, Gereon Elvers, Dulhan Jayalath · huggingface.co
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