The 2026 PNPL Competition builds on the 2025 PNPL effort by expanding the LibriBrain dataset to 32 new subjects and more within‑subject data, creating LibriBrain100. It introduces two tracks: a Deep track for high‑performance within‑subject word classification and a Broad track that tests cross‑subject generalisation with progressively less subject‑specific fine‑tuning data, down to about 10 minutes. The goal is to move toward a practical, non‑invasive brain‑computer interface that can restore communication for people with profound paralysis.
LibriBrain100 is a new large‑scale MEG dataset for speech decoding that contains over 100 hours of high‑quality recordings while subjects listened to naturalistic continuous speech. The dataset more than doubles the size of the original LibriBrain release, with a record 80 hours from a single subject and additional 40‑minute recordings from 32 subjects. The authors demonstrate the value of deep within‑subject data and broad multi‑subject data by achieving state‑of‑the‑art word‑classification performance and showing that supervised fine‑tuning can compensate for limited per‑subject data, all supported by open‑source tools and a public competition leaderboard.
By Francesco Mantegna, Dulhan Jayalath, Gereon Elvers, Tasha Kim, Benjamin Ballyk, Alex Fung, SungJun Cho, Teyun Kwon, Luisa Kurth, Miran \"Ozdogan, Gilad Landau, Pratik Somaiya, Natalie Voets, Mark Woolrich, Oiwi Parker Jones
The paper introduces a Cross-Subject Perceived Speech Decoding (CPSD) framework that tackles the challenge of decoding perceived speech from non‑invasive brain recordings across different subjects. CPSD uses a two‑stage training process: first, contrastive learning pre‑trains a source model on multiple subjects to capture shared representations; second, personal specialization fine‑tunes the model for a target subject by extracting consistent components and further training on that subject’s data. A Positional Encoding‑based Spatial Attention (PESA) module is added to remap MEG/EEG data into a standardized reference space, improving cross‑subject consistency. Evaluations on three datasets (Armeni 2022, PKUEEG 2025, Broderick 2018) show that CPSD outperforms baseline methods by more than 6.8%, 15.4%, and 15.8% in Top‑10 accuracy, demonstrating its effectiveness, efficiency, and robustness.
By Aoke Zhang, Bo Wang, Xihong Wu, Heping Cheng, Jing Chen
arXiv:2609.36736v1 Announce Type: cross
Abstract: Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative in...
By Moein Khajehnejad, Forough Habibollahi, Tommaso Boccato, Margarida Sousa, Michal Olak, Francesco Jamal Sheiban, Matteo Ferrante
The paper introduces Open‑Vocabulary Mutual Information (OVMI), an information‑theoretic metric that quantifies how much of a user’s intended speech a speech brain‑computer interface (BCI) can convey relative to a reference word distribution. OVMI enables comparison of systems that use different vocabularies, recording methods, and datasets, revealing that conventional metrics like accuracy and word error rate can overstate performance. Using OVMI, the authors compare existing speech BCI systems, expose trade‑offs between vocabulary coverage and decoding accuracy, and show that optimizing vocabulary selection for OVMI can improve accuracy by up to 16.3% across three speech domains.
By Dulhan Jayalath, Benjamin Ballyk, Oiwi Parker Jones
arXiv:2609.21288v1 Announce Type: new
Abstract: Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data se...
By Chenqian Le, Beatrice Fumagalli, Yasamin Esmaeili, Xupeng Chen, Tianyu He, Nikasadat Emami, Adeen Flinker, Yao Wang