15 citations · 29 across the 25 of their papers we have counts for
31 papers
Beyond Accuracy: Information-Theoretic Decoder Evaluation Under Latent Intent
Tasha Kim, Benjamin Ballyk, Anson Lei +2
A central challenge in assistive brain-to-robot control is decoding a user's latent intent from complex and noisy brain signals. Prior approaches evaluate decoders as single-trial…
MEG-Mamba: A Scalable State-Space Foundation Model for Magnetoencephalography
Chetan Gohil, SungJun Cho, Oiwi Parker Jones +1
Magnetoencephalography (MEG) is an imaging technique that offers a non-invasive, millisecond-resolution view of human brain activity. The increasing availability of MEG data presen…
Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text
Dulhan Jayalath, Oiwi Parker Jones
We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the influential work of d'Ascoli e…
The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding
Gilad D. Landau, Dulhan Jayalath, Oiwi Parker Jones
Non-invasive speech decoding remains constrained by the low signal-to-noise ratio of neural recordings, which makes fine-grained reconstruction of phonemes or individual words diff…
A Roadmap for MEG Foundation Models
Philipp Thölke, Hamza Abdelhedi, Yorguin Mantilla-Ramos +7
Foundation models are beginning to reshape brain-signal analysis by moving the field beyond task-specific decoding pipelines toward reusable models pretrained on broad neural datas…
The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100
Francesco Mantegna, Gereon Elvers, Dulhan Jayalath +18
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…
A Common Measure of Communication for Speech Brain-Computer Interfaces
Dulhan Jayalath, Benjamin Ballyk, Oiwi Parker Jones
Speech brain-computer interfaces (speech BCIs) translate neural activity into language, offering a path towards restoring speech for people with paralysis and, more broadly, enabli…
LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale
Francesco Mantegna, Dulhan Jayalath, Gereon Elvers +12
We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. LibriBrain100 more than doubles the…
Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech
Benjamin Ballyk, Teyun Kwon, Miran Özdogan +1
Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurgery. Existing MEG- and EEG-b…
A Systematic Evaluation of Sample-Level Tokenization Strategies for MEG Foundation Models
SungJun Cho, Chetan Gohil, Rukuang Huang +2
Recent success in natural language processing has motivated growing interest in large-scale foundation models for neuroimaging data. Such models often require discretization of con…
MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training
Dulhan Jayalath, Oiwi Parker Jones
Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient generalisation by learn…
MEGnifying Emotion: Sentiment Analysis from Annotated Brain Data
Brian Liu, Oiwi Parker Jones
Decoding emotion from brain activity could unlock a deeper understanding of the human experience. While a number of existing datasets align brain data with speech and with speech t…
Gated Uncertainty-Aware Runtime Dual Invariants for Neural Signal-Controlled Robotics
Tasha Kim, Oiwi Parker Jones
Safety-critical assistive systems that directly decode user intent from neural signals require rigorous guarantees of reliability and trust. We present GUARDIAN (Gated Uncertainty-…
Elementary, My Dear Watson: Non-Invasive Neural Keyword Spotting in the LibriBrain Dataset
Gereon Elvers, Gilad Landau, Oiwi Parker Jones
Non-invasive brain-computer interfaces (BCIs) are beginning to benefit from large, public benchmarks. However, current benchmarks target relatively simple, foundational tasks like…
MEG-GPT: A transformer-based foundation model for magnetoencephalography data
Rukuang Huang, Sungjun Cho, Chetan Gohil +2
Modelling the complex spatiotemporal patterns of large-scale brain dynamics is crucial for neuroscience, but traditional methods fail to capture the rich structure in modalities su…
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
Gilad Landau, Miran Özdogan, Gereon Elvers +15
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 communicat…
LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale
Miran Özdogan, Gilad Landau, Gereon Elvers +5
LibriBrain represents the largest single-subject MEG dataset to date for speech decoding, with over 50 hours of recordings -- 5 larger than the next comparable dataset and…
Unlocking Non-Invasive Brain-to-Text
Dulhan Jayalath, Gilad Landau, Oiwi Parker Jones
Despite major advances in surgical brain-to-text (B2T), i.e. transcribing speech from invasive brain recordings, non-invasive alternatives have yet to surpass even chance on standa…
NeuGPT: Unified multi-modal Neural GPT
Yiqian Yang, Yiqun Duan, Hyejeong Jo +6
This paper introduces NeuGPT, a groundbreaking multi-modal language generation model designed to harmonize the fragmented landscape of neural recording research. Traditionally, stu…
Resolving Domain Shift For Representations Of Speech In Non-Invasive Brain Recordings
Jeremiah Ridge, Oiwi Parker Jones
Machine learning techniques have enabled researchers to leverage neuroimaging data to decode speech from brain activity, with some amazing recent successes achieved by applications…
Non-invasive Neural Decoding in Source Reconstructed Brain Space
Yonatan Gideoni, Ryan Charles Timms, Oiwi Parker Jones
Non-invasive brainwave decoding is usually done using Magneto/Electroencephalography (MEG/EEG) sensor measurements as inputs. This makes combining datasets and building models with…
The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning
Dulhan Jayalath, Gilad Landau, Brendan Shillingford +2
The past few years have seen remarkable progress in the decoding of speech from brain activity, primarily driven by large single-subject datasets. However, due to individual variat…
Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian
Kaavya Chaparala, Guido Zarrella, Bruce Torres Fischer +2
In this paper we address the challenge of improving Automatic Speech Recognition (ASR) for a low-resource language, Hawaiian, by incorporating large amounts of independent text dat…
GPT2MEG: Quantizing MEG for Autoregressive Generation
Richard Csaky, Mats W. J. van Es, Oiwi Parker Jones +1
Foundation models trained with self-supervised objectives are increasingly applied to brain recordings, but autoregressive generation of realistic multichannel neural time series r…