arXiv subjects

Pierre Bourdillon

Publications and source records attributed to Pierre Bourdillon.

6 recordsLinked to original sources

arXiv2026

Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings

Restoring communication for people who have lost the ability to speak or move after a brain injury is a major challenge. While intracranial implants now enable high-performing brain-computer-interfaces, non-invasive alternatives are still lagging behind. Here, we present Brain2Qwerty v2, a model that can decode the production of natural sentences solely from real-time magnetoencephalography (MEG) recordings. By collecting 22,000 sentences typed by nine subjects, each recorded for 10 hours, our model leverages character, word and sentence-level representations to achieve an average word error rate (WER) of 39%. For our best participant, the model accurately decodes half of the sentences with one word error or less. Critically, decoding accuracy log-linearly improves with data volume, suggesting that the performance gap with intracranial approaches could be partially bridged through data scaling. We show that AI enables this performance in three main ways: the substitution of hand-crafted pipelines for event detection with deep learning, the finetuning of large language models to extract semantic representations, and the deployment of AI agents to iteratively refine our decoding pipeline via automated code development. Together, these results show that non-invasive brain-to-text decoding starts to operate at a level of accuracy previously thought exclusive to surgical implants, opening a path toward safe and efficient brain-computer-interfaces.

cs.CL↗

arXiv2026

Temporal structure of the language hierarchy within small cortical patches

Speech production requires the rapid coordination of a complex hierarchy of linguistic units, transforming a semantic representation into a precise sequence of articulatory movements. To unravel the neural mechanisms underlying this feat, we leverage recordings from eight 3.2 x 3.2 mm 64-microelectrode arrays implanted in the motor cortex and inferior frontal gyrus of two patients tasked to produce twenty thousand sentences. We show that a hierarchy of linguistic features are robustly encoded in most of these small cortical patches. Contrary to our expectations, instead of a clear macroscopic organization between patches, we observe a multiplexing of phonetic, syllabic and lexical representations within each cortical patch. Critically, this coding scheme dynamically changes over time to allow successive phonemes, syllables and words to be simultaneously represented without interference. Overall, these results, reminiscent of position encoding in transformers, show how small cortical patches organize the unfolding of the speech hierarchy during language production.

q-bio.NC↗

arXiv2025

From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining

Decoding speech from brain activity has typically relied on limited neural recordings collected during short and highly controlled experiments. Here, we introduce a framework to leverage week-long intracranial and audio recordings from patients undergoing clinical monitoring, effectively increasing the training dataset size by over two orders of magnitude. With this pretraining, our contrastive learning model substantially outperforms models trained solely on classic experimental data, with gains that scale log-linearly with dataset size. Analysis of the learned representations reveals that, while brain activity represents speech features, its global structure largely drifts across days, highlighting the need for models that explicitly account for cross-day variability. Overall, our approach opens a scalable path toward decoding and modeling brain representations in both real-life and controlled task settings.

cs.SD↗

arXiv2025

Emergence of Language in the Developing Brain

A few million words suffice for children to acquire language. Yet, the brain mechanisms underlying this unique ability remain poorly understood. To address this issue, we investigate neural activity recorded from over 7,400 electrodes implanted in the brains of 46 children, teenagers, and adults for epilepsy monitoring, as they listened to an audiobook version of "The Little Prince". We then train neural encoding and decoding models using representations, derived either from linguistic theory or from large language models, to map the location, dynamics and development of the language hierarchy in the brain. We find that a broad range of linguistic features is robustly represented across the cortex, even in 2-5-year-olds. Crucially, these representations evolve with age: while fast phonetic features are already present in the superior temporal gyrus of the youngest individuals, slower word-level representations only emerge in the associative cortices of older individuals. Remarkably, this neuro-developmental trajectory is spontaneously captured by large language models: with training, these AI models learned representations that can only be identified in the adult human brain. Together, these findings reveal the maturation of language representations in the developing brain and show that modern AI systems provide a promising tool to model the neural bases of language acquisition.

q-bio.NC↗

arXiv2025

From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production

Humans effortlessly communicate their thoughts through intricate sequences of motor actions. Yet, the neural processes that coordinate language production remain largely unknown, in part because speech artifacts limit the use of neuroimaging. To elucidate the unfolding of language production in the brain, we investigate with magnetoencephalography (MEG) and electroencephalography (EEG) the neurophysiological activity of 35 skilled typists, while they typed sentences on a keyboard. This approach confirms the hierarchical predictions of linguistic theories: the neural activity preceding the production of each word is marked by the sequential rise and fall of context-, word-, syllable-, and letter-level representations. Remarkably, each of these neural representations is maintained over long time periods within each level of the language hierarchy. This phenomenon results in a superposition of successive representations that is supported by a hierarchy of dynamic neural codes. Overall, these findings provide a precise computational breakdown of the neural dynamics that coordinate the production of language in the human brain.

q-bio.NC↗

arXiv2024

New modalities of cortical electrophysiology, perspectives in medical research and human physiology

Recent advances in material technology and in micro- and nano-electronics have profoundly changed the design of intracranial electrophysiology electrodes. It is now possible to manufacture electrodes that record cortical activity at a spatial resolution that was previously unthinkable. This high spatial resolution enables recording of the functional structures of the brain, and differentiation of the activity of the different types of neurons composing them. In this paper, we present a review of the different types of electrodes now available, and then suggest one of the first applications for such high resolution electrodes, namely a means to better characterise the mechanisms that generate focal seizures in epileptics. Finally, we reflect more broadly on prospects for their future use.

q-bio.NC↗