# The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

arXiv: https://arxiv.org/abs/2609.03231 · HTML: https://mlsanity.com/paper/2609.03231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 12 of 12)

- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Achieving Human Parity in Conversational Speech Recognition](https://mlsanity.com/paper/1610.05256/) (2016)
- [Are EEG-to-Text Models Working?](https://mlsanity.com/paper/2405.06459/) (2024)
- [The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning](https://mlsanity.com/paper/2406.04328/) (2024)
- [LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale](https://mlsanity.com/paper/2506.02098/) (2025)
- [The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset](https://mlsanity.com/paper/2506.10165/) (2025)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [Brain-to-Text Benchmark '24: Lessons Learned](https://mlsanity.com/paper/2412.17227/) (2024)
- [MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training](https://mlsanity.com/paper/2602.02494/) (2026)
- [LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale](https://mlsanity.com/paper/2608.25204/) (2026)
- [Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings](https://mlsanity.com/paper/2608.18114/) (2026)
- [A Common Measure of Communication for Speech Brain-Computer Interfaces](https://mlsanity.com/paper/2609.02887/) (2026)

## Built on by (most cited first, 2 of 2)

- [Brain-to-Language Decoding: Tasks, Signals, Methods, Evaluation, Practical Use and Beyond](https://mlsanity.com/paper/2609.27650/) (2026)
- [Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2609.40359/) (2026)

## Abstract (arXiv)

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. Winning submissions reached F1-macro scores of 95.6% and 73.6% on the respective tasks (Elvers et al., 2026), highly significant advances. This success was built on the LibriBrain dataset ("Ozdogan et al., 2025), the largest within-subject MEG dataset recorded at the time with ~50 hours of data for one subject. However, while within-subject scale drives strong decoding performance, a practical BCI must generalise to new users from minutes of data, not hours. The 2026 PNPL competition responds to this challenge with LibriBrain100 (Mantegna et al., 2026), an extended LibriBrain dataset with 32 additional subjects (~40 minutes each) plus even more within-subject data (~80 hours). Advancing the curriculum of tasks to focus on word classification, two complementary tracks are presented in this competition: the Deep track targets within-subject word classification at scale, aiming at the best possible performance; the Broad track targets cross-subject generalisation, progressively reducing the amount of subject-specific fine-tuning data from ~40 to ~20 to ~10 minutes, a duration that falls within a clinically feasible range and brings us a step closer to a non-invasive BCI capable of restoring communication to people living with profound paralysis.