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
The article surveys brain‑to‑language decoding, covering tasks, neural signals, methods, and evaluation across invasive and non‑invasive modalities. It traces the field’s evolution from constrained recognition to text generation, streaming speech, and facial animation, linking tasks to neural populations and decoder representations. The review highlights complementary decoding targets, shared representations, and the growing importance of calibration, feedback, and user control for online communication, while proposing a five‑level future trajectory toward bidirectional cognitive exchange.
By Yiqian Yang, Yiqun Duan, Chenyu Liu, Yiqi Wang, Xinliang Zhou, Chin-Teng Lin, Yu Zhang
This scoping review examines how brain‑computer interfaces (BCIs) can restore sensory and motor functions in people with severe neurological impairment. It introduces a unified 2×2 framework (invasiveness × signal direction) to map 31 key studies, highlighting that most evidence comes from invasive, efferent‑restoration approaches post‑2015. The paper outlines a roadmap toward closed‑loop, bidirectional restoration and identifies gaps in metric standardization, longitudinal data, and cross‑community collaboration.
By Xuan-The Tran
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 10 minutes. The competition aims to advance non‑invasive brain‑computer interfaces toward practical, clinically feasible communication restoration for people with profound paralysis.
By Francesco Mantegna, Gereon Elvers, Dulhan Jayalath, Gilad Landau, Tasha Kim, Miran \"Ozdogan, Luisa Kurth, Teyun Kwon, SungJun Cho, Benjamin Ballyk, Alex Fung, Anna Greer, Pratik Somaiya, Christian Herff, Yorguin Mantilla Ramos, Hamza Abdelhedi, Karim Jerbi, Greg Farquhar, Brendan Shillingford, Mark Woolrich, Oiwi Parker Jones
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
arXiv:2608. 13576v1 Announce Type: cross Abstract: Brain-computer interface (BCI) research relies on multistage computational pipelines, yet progress remains constrained by fragmented data formats, heterogeneous decoder implementations and hardware-specific deployment toolchains, and researchers lack an integrated workflow.
By Liyuan Han, Xinrui Yang, Tianyu Zheng, Qizhi Yang, Yitao Qin, Liang Chen, Qinglai Wei, Binjie Hong, Xinhe Zhang, Rui Xiong, Yong Gu, Mu-ming Poo, Bo Xu, Chengyu Li, Tielin Zhang
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.
arXiv:2603. 17109v2 Announce Type: replace Abstract: Decoding brain activity into natural language is a major challenge in AI with important applications in assistive communication, neurotechnology, and human-computer interaction.
By Akshaj Murhekar, Christina Liu, Abhijit Mishra, Shounak Roychowdhury, Jacek Gwizdka
arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.
By Yuanhao Chen, Peter Chin
Brain2Qwerty v2 is a model that decodes natural sentences from real‑time magnetoencephalography (MEG) recordings, achieving an average word error rate of 39% across 22,000 sentences typed by nine subjects. The model uses character, word, and sentence‑level representations and shows that decoding accuracy improves log‑linearly with more data, narrowing the gap to intracranial brain‑computer interfaces. AI contributes by replacing hand‑crafted event detection with deep learning, fine‑tuning large language models for semantic extraction, and employing AI agents to refine the decoding pipeline through automated code development.
By Mingfang Zhang, Jarod L\'evy, Cedric Rommel, J\'er\'emy Rapin, Corentin Bel, Julie Bonnaire, Daniel Nieto, Pierre Bourdillon, Svetlana Pinet, St\'ephane d'Ascoli, Thomas Moreau, Jean-R\'emi King
arXiv:2607. 05165v1 Announce Type: new Abstract: Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurgery.
By Benjamin Ballyk, Teyun Kwon, Miran \"Ozdogan, Oiwi Parker Jones
arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.
By Siyang Li, Jiayi Ouyang, Zhenyao Cui, Ziwei Wang, Tianwang Jia, Feng Wan, Dongrui Wu